Scientometric Study on Intelligent Maintenance Systems for … · 2018. 7. 17. · Scientometric...

Defence Research and Development Canada Contract Report DRDC-RDDC-2018-C122 June 2018

CAN UNCLASSIFIED

CAN UNCLASSIFIED

Scientometric Study on Intelligent Maintenance Systems for Military Platforms

Maude Lethiecq-Normand and Rebecca Jansen Intelligence and Analytics NRC-Knowledge Management Prepared by: NRC-Knowledge Management 1200 Montreal Road Ottawa, ON K1A 0R6 NRC-National Science Library Project No: MLN18-35 Technical Authority: Ian Thompson; DRDC – Atlantic Research Centre Contractor's date of publication: March 2018

Template in use: CR_EL1%20Advanced%20Template_EN%202018-04_04-v1_WW.DOTM

© Her Majesty the Queen in Right of Canada (Department of National Defence), 2018

© Sa Majesté la Reine en droit du Canada (Ministère de la Défense nationale), 2018

CAN UNCLASSIFIED

CAN UNCLASSIFIED

IMPORTANT INFORMATIVE STATEMENTS

This document was reviewed for Controlled Goods by Defence Research and Development Canada (DRDC) using the Schedule to the Defence Production Act.

Disclaimer: This document is not published by the Editorial Office of Defence Research and Development Canada, an agency of the Department of National Defence of Canada but is to be catalogued in the Canadian Defence Information System (CANDIS), the national repository for Defence S&T documents. Her Majesty the Queen in Right of Canada (Department of National Defence) makes no representations or warranties, expressed or implied, of any kind whatsoever, and assumes no liability for the accuracy, reliability, completeness, currency or usefulness of any information, product, process or material included in this document. Nothing in this document should be interpreted as an endorsement for the specific use of any tool, technique or process examined in it. Any reliance on, or use of, any information, product, process or material included in this document is at the sole risk of the person so using it or relying on it. Canada does not assume any liability in respect of any damages or losses arising out of or in connection with the use of, or reliance on, any information, product, process or material included in this document.

Scientometric Study on Intelligent Maintenance Systems March, 2018

NRC-NATIONAL SCIENCE LIBRARY PAGE 3

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY ................................................................................................................................... 7

2 BACKGROUND ............................................................................................................................................. 10

2.1 CONTEXT ....................................................................................................................................................... 10 2.2 KEY ISSUES ..................................................................................................................................................... 10 2.3 KEY QUESTIONS .............................................................................................................................................. 11

3 INTRODUCTION ........................................................................................................................................... 12

3.1 PROJECT DATASETS & STATISTICS ....................................................................................................................... 12 3.2 TOPICS (SUBJECT GROUPS) & META-CATEGORIES ................................................................................................. 13 3.3 CONCEPTS & DEFINITIONS ................................................................................................................................ 15

3.3.1 Intelligent Maintenance Systems (IMS) ................................................................................................. 17 3.3.2 Health Assessment Technologies & Condition-Based Monitoring ......................................................... 22

4 SIGNIFICANT POLICIES, STRATEGIES AND ROADMAPS ................................................................................. 24

4.1 IMS DATASET ................................................................................................................................................ 25 4.2 STANDARDS ................................................................................................................................................... 26

4.2.1 SAE ......................................................................................................................................................... 26 4.2.2 NATO ...................................................................................................................................................... 28

4.3 US ............................................................................................................................................................... 29 4.4 UK ............................................................................................................................................................... 32 4.5 AUSTRALIA ..................................................................................................................................................... 34 4.6 FRANCE ......................................................................................................................................................... 35 4.7 GERMANY ...................................................................................................................................................... 35 4.8 NEW ZEALAND ............................................................................................................................................... 36 4.9 NETHERLANDS ................................................................................................................................................ 36 4.10 NORWAY ....................................................................................................................................................... 37 4.11 SWEDEN ........................................................................................................................................................ 37 4.12 OTHERS ......................................................................................................................................................... 37

5 MAJOR THEMES AND TOPIC CORRELATIONS ............................................................................................... 39

5.1 TOP PLATFORMS ............................................................................................................................................. 39 5.2 TOP TOPICS ................................................................................................................................................... 41

5.2.1 IMS ......................................................................................................................................................... 41 5.2.2 Navy/Maritime ...................................................................................................................................... 46

6 EMERGING RESEARCH TOPICS ..................................................................................................................... 51

6.1 RESEARCH MOMENTUM ................................................................................................................................... 51 6.1.1 Components ........................................................................................................................................... 54 6.1.2 Conditions .............................................................................................................................................. 57 6.1.3 Functions ................................................................................................................................................ 59 6.1.4 Technologies .......................................................................................................................................... 63 6.1.5 Statistical/Predictive methods ............................................................................................................... 66 6.1.6 Communications .................................................................................................................................... 68

6.2 RESEARCH MOMENTUM: NAVY/MARITIME ......................................................................................................... 70 6.2.1 Navy/Maritime: Components ................................................................................................................ 73 6.2.2 Navy/Maritime: Conditions .................................................................................................................... 75 6.2.3 Navy/Maritime: Functions ..................................................................................................................... 76 6.2.4 Navy/Maritime: Technologies, Communications & Statistical/Predictive methods .............................. 78

6.3 DIGITAL TWINS ............................................................................................................................................... 80



7 DRIVERS & BARRIERS TO ADOPTION ........................................................................................................... 83

7.1 DRIVERS & BENEFITS ....................................................................................................................................... 83 7.2 BARRIERS & CHALLENGES ................................................................................................................................. 85 7.3 RETURN ON INVESTMENT .................................................................................................................................. 88 7.4 COMMUNICATION REQUIREMENTS/STANDARDS ................................................................................................... 95

8 KEY PLAYERS.............................................................................................................................................. 101

8.1 TOP COUNTRIES ............................................................................................................................................ 101 8.2 TOP PLAYERS AND THEIR EXPERTISE .................................................................................................................. 103

8.2.1 Top Global Players ............................................................................................................................... 103 8.2.2 Top Players: Military ............................................................................................................................ 108 8.2.3 Top Allied Countries Players ................................................................................................................. 111 8.2.4 Top Canadian Players........................................................................................................................... 113

9 CONCLUSIONS ........................................................................................................................................... 115

10 REFERENCES .............................................................................................................................................. 117

11 APPENDICES .............................................................................................................................................. 126

11.1 ATTACHMENTS ............................................................................................................................................. 126 11.2 ACRONYMS .................................................................................................................................................. 126 11.3 METHODOLOGY ............................................................................................................................................ 127

11.3.1 Sources ............................................................................................................................................ 127 11.3.2 Searches........................................................................................................................................... 128 11.3.3 Analysis ............................................................................................................................................ 128 11.3.4 R&D Momentum .............................................................................................................................. 129 11.3.5 Social Network Analysis ................................................................................................................... 130

11.4 ESTABLISHING AND IMPLEMENTING CBMA CAPABILITIES: INPUTS REQUIRED AND OUTPUTS GENERATED ....................... 131 11.5 IMS: INDIRECT IMPACTS ................................................................................................................................. 133

List of Figures

Figure 1. Mapping of Topics to Meta-Categories ........................................................................................................ 14 Figure 2. IMS Mode24 ................................................................................................................................................... 20 Figure 3. University of South Carolina's Preventive Predictive Maintenance Approach ............................................. 21 Figure 4. # Publications by Document Type and Platform, Limited to the Military Dataset ....................................... 25 Figure 5. # Publications by Country and Document Type, Limited to the Military Dataset ........................................ 26 Figure 6. DoD Condition Based Maintenance Plus Model41 ........................................................................................ 30 Figure 7. US DoD Navy Integrated Condition Assessment System Model47 ................................................................ 32 Figure 8. # Publications by Platforms .......................................................................................................................... 39 Figure 9. Publication Timeline ..................................................................................................................................... 40 Figure 10. Top Topics ................................................................................................................................................... 41 Figure 11. Top Topics by Top Platforms....................................................................................................................... 42 Figure 12. Publications Topic Autocorrelation Map .................................................................................................... 43 Figure 13. Top Topics by Top Components (publication count) .................................................................................. 45 Figure 14. Top Topics by Top Condition (publication count) ....................................................................................... 46 Figure 15. Top Topics: Navy/Maritime Dataset ........................................................................................................... 47 Figure 16. Publications Topic Autocorrelation Map: Navy/Maritime Dataset ............................................................ 48 Figure 17. Top Topics by Components: Navy/Maritime Dataset ................................................................................. 49 Figure 18. Top Topics by Conditions: Navy/Maritime Dataset .................................................................................... 50 Figure 19. Full Research Momentum........................................................................................................................... 52 Figure 20. List of Emerging and Disappearing/Brand new Topics ............................................................................... 53



Figure 21. R&D Momentum: Components .................................................................................................................. 54 Figure 22. R&D Momentum: Components (Emerging Quadrant) ............................................................................... 55 Figure 23. R&D Momentum: Components (Disappearing/Brand new Quadrant) ...................................................... 57 Figure 24. R&D Momentum: Conditions ..................................................................................................................... 58 Figure 25. R&D Momentum: Functions ....................................................................................................................... 60 Figure 26. R&D Momentum: Functions (Emerging Quadrant) .................................................................................... 60 Figure 27. R&D Momentum: Functions (Disappearing/Brand new Quadrant) ........................................................... 62 Figure 28. R&D Momentum: Technologies ................................................................................................................. 63 Figure 29. R&D Momentum: Technologies (Emerging Quadrant) ............................................................................... 64 Figure 30. R&D Momentum: Technologies (Disappearing/Brand New Quadrant) ..................................................... 66 Figure 31. R&D Momentum: Statistical/Predictive methods ...................................................................................... 67 Figure 32. R&D Momentum: Communications ........................................................................................................... 69 Figure 33. R&D Momentum: Navy/Maritime .............................................................................................................. 71 Figure 34. Navy/Maritime: List of Emerging and Disappearing/Brand new Topics (by publication count) ................ 72 Figure 35. R&D Momentum: Navy/Maritime (Components) ...................................................................................... 73 Figure 36. R&D Momentum: Navy/Maritime (Conditions) ......................................................................................... 75 Figure 37. R&D Momentum: Navy/Maritime (Functions) ........................................................................................... 77 Figure 38. R&D Momentum: Navy/Maritime (Technologies, Statistical/Predictive methods & Communications) .... 79 Figure 39. Naval Digital Twin Framework Concept ...................................................................................................... 81 Figure 40: Benefits of Implementation145 .................................................................................................................... 85 Figure 41. OSA-CMB Functional Blocks7 ...................................................................................................................... 97 Figure 42: Communication between two IVHM Components using RMI-Java middleware and OSA-CBM data model 165 ................................................................................................................................................................................. 98 Figure 43. Top Countries, # Publications, 2012-Present ............................................................................................ 101 Figure 44. # Publications: Allied Countries by Top Topics ......................................................................................... 102 Figure 45. Canadian Top 20 Topics: # Publications .................................................................................................... 103 Figure 46. Top Players, # Publications, 2012-Present ................................................................................................ 104 Figure 47. Top Players: Allied Countries, # Publications, 2012-Present .................................................................... 105 Figure 48. Top Topics by Top Affiliations from Allied Countries ................................................................................ 106 Figure 49. Collaboration Networks ............................................................................................................................ 107 Figure 50. Collaboration Networks: US DoD Cluster ................................................................................................. 108 Figure 51. Top Players: Military Dataset, # Publications, 2012-Present .................................................................... 109 Figure 52. Top Topics by Top Military Affiliations ..................................................................................................... 110 Figure 53. Collaboration Networks: Military Publications Dataset............................................................................ 111 Figure 54. Topics and Players Co-occurrence Map, Navy/Maritime Dataset, Canada-US-UK-Australia ................... 112 Figure 55. Top Players: Canada, # Publications, 2012-Present .................................................................................. 113 Figure 56. Top Canadian Affiliations by Top Topics ................................................................................................... 114 Figure 57. The Research Momentum Indicator ......................................................................................................... 129 Figure 58: Input Requirements to Establish CBMa Capabilities 158 ............................................................................ 131 Figure 59: Outputs of Implemented CBMa Capabilities158 ........................................................................................ 132

List of Tables

Table 1. Summary of Findings ........................................................................................................................................ 7 Table 2. Meta-Category Definitions ............................................................................................................................. 13 Table 3. Maintenance Strategy .................................................................................................................................... 15 Table 4. IMS Processes ................................................................................................................................................ 19 Table 5. Problems solved via IMS ................................................................................................................................ 20 Table 6. Examples of Military Maintenance Projects Led by the Private Sector ......................................................... 24 Table 7. Identified Relevant NATO Standards ............................................................................................................. 28 Table 8. US DoD CMB+ Projects ................................................................................................................................... 30 Table 9: CBM+ Objectives and Metrics 142 ................................................................................................................... 83



Table 10. Costs and Savings Resulting from Inputs Required for CBMa Capabilities .................................................. 89 Table 11. CBM Output Costs and Savings with Direct Impacts158 ................................................................................ 91 Table 12: Examples of Standards Available to CBM+ Implementers 142 ...................................................................... 99 Table 13. List of Attachments .................................................................................................................................... 126 Table 14. Search Terms .............................................................................................................................................. 128 Table 15: IMS: Indirect Impacts ................................................................................................................................. 133



1 EXECUTIVE SUMMARY In order to assist with long-term R&D planning and the prioritization of research topics, a scientometric study was commissioned to provide a high level overview of worldwide research activity in intelligent maintenance systems for military platforms. This study will assist DRDC in uncovering and understanding the potential impact of new research on future defense maintenance operations. In all, 4,753 publications from 2012-2018 were retrieved and analyzed using text mining software and a variety of visualization tools to identify emerging trends, key players, collaboration networks, platform areas and policies. Table 1 presents the key findings of the study.

Table 1. Summary of Findings

Key Question Key Findings

Definition Intelligent Maintenance Systems (IMS) are advanced predictive maintenance systems that integrate both diagnostics and prognostics. They enable a “smart” or “intelligent” approach to identifying, scheduling and performing maintenance tasks based on a holistic real-time understanding of all critical components in a platform. IMS use data from sophisticated sensors, computerized maintenance tools and monitoring equipment to enable a continuous, seamless and meaningful flow of information throughout the maintenance process with the goal of tracking health degradation to predict which components of a machine are likely to fail, and when. The condition of individual components are considered collectively in relation to each other in maintenance planning. It is essential to note that all types of maintenance remain necessary for an effective maintenance program, nevertheless, the need for corrective and preventative maintenance is reduced, along with maintenance costs, with a successful intelligent maintenance approach1-3

Emerging Trends The topic groups with the highest R&D momentum within each meta-category are listed below for the full IMS dataset and for the Navy/Maritime subset. Most of these topics are briefly reviewed in the report.

Overall Dataset Navy/Maritime Dataset Components

Axles Components

Gases Conditions

Calibration Pressure

Conditions

Sensitivity Security

Functions

Defect detections Failure detections

Functions

Planning Failure analysis

Technologies

Internet of things Digital twins

Technologies

Remote detection

Statistical/Predictive methods

Event/Fault trees Markov methods Monte Carlo methods

Statistical/ Predictive methods

Time series analysis

Communications

Bandpass filters Network architectures

Communications

Data acquisition




Digital Twins A digital twin (DT) uses integrated multiphysics models, sensor information and input data from an in-service vehicle to mirror and predict the life of its corresponding physical twin.4 This topic group has the third-largest growth rate in the dataset.

Themes found in the 2017 Digital Twin topic group articles include: Prognostics Bayesian methods Damage detection

Digital twins are of current interest to the US military, which is investigating Navy Digital Twins through a collaborative project.5 Also, the US Navy’s Military Sealift Command for which GE will build a digital twin to compare real-time asset performance to spot data variance.6

Barriers & Challenges Drivers for adoption of IMS in the military include, but are not limited to:

Improvement of maintenance agility and responsiveness Increasing operational availability and readiness Reducing lifecycle total ownership costs

Barriers to adoption of IMS in the military include, but are not limited to:

Cost of implementation Dispersed nature of military platforms Analysis of large amounts of data Technological barriers, such as data reliability and prognostics

Return on Investment (ROI) has been demonstrated for IMS, but varies by asset:

ROI will vary depending on the specific platform and the level of IMS implemented

A thorough understanding of the costs associated with a specific platform and its components/systems is required

There is no standard template to establish a business case for ROI (must consider unique qualities of each asset and organization)

Identified communications requirements/standards are:

ISO, SAE, IEEE, MIMOSA, among others, need to be considered Open Systems Architecture for Condition-Based Maintenance (OSA-

CBM) describes a standardized information delivery system for Condition Based Maintenance, and thus IMS.7

Key Players Top international players include:

US DoD, USA (153 publications, expertise in Health assessment/monitoring, Sensors, Structural Health Monitoring (SHM) and Integrated)

NASA, USA (101 publications, expertise in Health assessment/monitoring, Predictive, Prognosis, Statistical/Predictive methods and Sensors)

Cranfield University, UK (76 publications, expertise in Health assessment/monitoring, Integrated, Condition monitoring (CBMo) and Faults)

Top Canadian players include:

University of Alberta (17 publications all included in the Condition monitoring (CBMo) topic)




National Research Council (15 publications, expertise in Condition monitoring (CBMo) and Health assessment/monitoring)

Bombardier (12 publications, expertise in Condition monitoring (CBMo) and Engines/Propulsion systems)

Policies The majority of policy documents refer to subgroups of IMS technologies (ex. Structural Health Assessment) and are intended for aerospace platforms.

The US has the most detailed IMS policies (CBM+) which applies to all US military agencies (Army, Navy, Air Forces).

Military platform maintenance is being increasingly outsourced within the allied countries studied for this report.8

Recommendations for next steps are:

Attention should be given to exploring emerging topics such as Internet of things, Digital twins and other communications-based topics such as Bandpass filters and network architectures that will be necessary to integrate all components of IMS systems and provide a holistic real-time view of platforms and fleets, even when dispersed.

Further explore the applications of recent AI advances to IMS like the artificial immune system methods.

Do a technical review and deep dive analysis on topics of interest, specialized by platform. Since IMS technologies are not new, a technology readiness level (TRL) analysis by platform type

in the military context would help to better understand the adoption of IMS in these contexts. An analysis including research from allied partners and classified sources could be of particular interest.



2 BACKGROUND 2.1 Context In order to assist with long-term R&D planning and the prioritization of research in the domain of intelligent maintenance technologies, DRDC has commissioned this scientometric study, which will provide a high-level overview of worldwide research activity in the area of Intelligent Maintenance Systems (IMS). The report will first define Intelligent Maintenance Systems and identify relevant military policies or roadmaps. A discussion of emerging R&D trends, barriers/challenges and the key players will follow. Intelligent Maintenance Systems (IMS) are defined as technologies that enable a "smart" or "intelligent" approach to identifying, scheduling and performing maintenance tasks. It employs continuous or periodic assessments of platform conditions (health assessment monitoring/condition-based monitoring) using sensors or external tests and measurements. IMS are advanced condition-based maintenance systems which include a more proactive and predictive approach to maintenance also known as Predictive Maintenance (PdM), advanced Condition-based Maintenance (CBMa), e-maintenance or Artificial Immune Intelligent Maintenance System (AI2MS). These systems perform five tasks: Sense & Collect, Connect, Store, Analyze and Act. The focus of this study will be technologies related the Analyze and Act tasks. Technologies focused specifically on sensors, supply management and logistics are deemed out of scope. The study will explore IMS in a military context, but will also include all naval/maritime, aerospace and landa applications. It will not, however, discuss manufacturing or infrastructure maintenance. This study will detect and categorize the international R&D domains in the field of intelligent maintenance systems, as well as identify applications, challenges/barriers, and communication requirements for intelligent maintenance systems. It will include a broad survey of R&D, top research organizations and collaboration networks in the field of intelligent maintenance systems. This study is designed to provide DRDC with a comprehensive survey of research and state-of-the-art technologies in intelligent maintenance systems, in order to frame and inform future DRDC goals and projects related to the topic. It will not be a critical evaluation of the methods or effects of the technologies being reviewed, but will report on trends in the literature based on an analysis of the metadata associated with the bibliographic references. Results of this project will be used to identify domains that could present an area of interest for which DRDC may wish to develop expertise and to support the selection of future deep dive projects. More immediately, the results of this project will be used to inform an upcoming DRDC research project and related meetings.

2.2 Key Issues The objective of the study is to provide information to DRDC research scientists on recent advances in the area of Intelligent Maintenance Systems (IMS). This will allow them to identify strengths, fast moving research fronts, and gaps and to decide where the best investments could be made in terms of

a Note that the rail sector was limited to locomotive and engine maintenance only and excluded rail tracks and signaling components.



technology developments. This study will assist DRDC in uncovering and understanding the potential impact of new research on future defence and security capabilities and operations.

2.3 Key Questions 1. Intelligent maintenance systems (IMS)

a. What are intelligent maintenance systems?

b. What significant current policies, strategies, guidance, roadmaps have been issued by military organizations (allied countries) with regards to intelligent maintenance systems?

2. Current and emerging trends in the literature

a. What are the current and emerging intelligent maintenance systems/processes/technologies being investigated worldwide?

b. What are the current and emerging application areas (platforms) for intelligent maintenance systems?

c. How are intelligent maintenance systems linked to "digital twins" in a military environment, specifically for naval/maritime applications?

3. Barriers and Challenges

a. What are the barriers and drivers to adoption (i.e. policies, processes, organizational, technological, and infrastructure (PPOTI)) for intelligent maintenance systems from a military perspective?

b. What Return on Investment (ROI), cost savings or process efficiencies have been documented in indexed literature for military uses?

c. What are communication requirements and/or standards (i.e. interoperability assessment) that underlie intelligent maintenance system technologies, which need to be considered in a military environment?

4. Key players

a. Who are the key players (country level and organizational level) from Canada, allied countriesb and globally?

b. What are the top topics on which Canada and the top global players are publishing (with a focus on allied countries)?

c. What are the key collaborators publishing with the top players globally and in Canada?

5. For Trend Card only a. What could be the anticipated SPEED (Social, Political, Economic, Environmental and

Defence) impact associated with intelligent maintenance systems?c

b Allied countries for this study will be limited to and include UK, USA, Australia, France, Germany, New Zealand, Netherlands, Norway, and Sweden. c For the Emerging Technologies Snapshot.



3 INTRODUCTION 3.1 Project Datasets & Statistics The evidence presented in the analyses that follow is based on bibliographical references from scientific and technical publications, including conference proceedings, book chapters and publicly available military reports. Multiple databases were consulted: these are listed in the Appendix (see Section 12.3.1.). The searches and data imports were done in January 2018. Data from these sources were downloaded into a text mining platform,d where they were cleaned to remove variant forms of names and duplicate publications. Taken together, the data provides a composite picture of the research agenda (publications) in the field of Intelligent Maintenance Systems (IMS). The final dataset use for this study comprises 4,753 publications from 2012 to 2018. All articles were classified by platformse of interest as determined at the needs assessment. The classification was done based on keyword searches of each platform-related term found in the title, abstract, and descriptor fields of publications. The platform categories are: Navy/Maritime, Aerospace, Automotive, Military and Rail. One platform was added, Transportation – general, to group publications without any direct mention of a specific platform category, but that discuss transportation, fleet management, a vehicle component for a non-specified platform (i.e. engines or brakes) or general vehicle maintenance. Platform categories are not mutually exclusive. One subset was created for publications focusing on the Navy/Maritime platforms to answer key questions focusing on the maritime sector:

Navy/Maritime: publications related to the naval and maritime transportation sector (597 publications, 12.5% of the dataset).

The IMS publications dataset was further divided into two other non-mutually exclusive subsets to answer questions related to key players:

Allied countries: publications authored by at least one of the following allied countries selected for this project: UK, USA, Australia, France, Germany, New Zealand, Netherlands, Norway, and Sweden (2,220 publications, 47% of the dataset);

Canada: publications authored by at least one Canadian affiliated author (147 publications, 3% of the dataset).

Information sources found online through search engines searches and website browsing were also used to supplement the analyses from the dataset.

d VantagePoint, produced by US-based Search Technology. e A type of vehicle on which maintenance is applied.



3.2 Topics (Subject Groups) & Meta-Categories To enable identification of trends and advances in the domain of IMS, topics (subject groups) were created based on keyword searches of terms found in the title, abstract, and descriptor fields of publications. Essentially, this enables all articles that have the same (or synonymous) keywords in the above-mentioned publication fields to be analyzed together as a topic group. The rationale for the creation of these groups was based upon: the occurrence of words and phrases with high publication counts; the presence of concepts, technical features, or challenges highlighted in market literature or review articles; or the presence of terms suggested by the client, and highlighted in the mandate. The result was 258 topics covering 99.7% of the overall IMS dataset plus the six platform groups and 247 topics covering 98.8% of the Navy/Maritime IMS dataset. Topics (subject groups) are not mutually exclusive. For instance, an article on cybersecurity requirements for IMS applied to naval vessels would be assigned to several topic groups (i.e. cybersecurity and standards) and multiple platforms (i.e. Navy/Maritime and Military). A complete listing of topics (subject groups) is included in an Excel file attached to this report. Pursuant to guidance from the subject-matter experts from DRDC, some topics were identified as of particular interest (marked in the Excel file) and will be referred to as topics of interest throughout the report. When relevant, analysis will be provided on these topics. The topics were further classified into one of six meta-categories presented and defined in Table 2. The Meta-categories make the analysis easier by way of grouping and comparing similar topics that exist within the dataset instead of only analyzing the dataset as a whole.

Table 2. Meta-Category Definitions

Meta-Category Definitions

Components Specific parts of the asset/platform on which IMS is applied.

Conditions Conditions that are monitored by the IMS or CBMo system.

Function The roles/functions of the IMS systems (i.e. what the system does to enable IMS).

Technologies Enabling technologies that support IMS functions and/or are embedded in IMS.

Statistical/Predictive methodsf Used to compute data and support decisions.

Communicationsf Communication systems, part of the IMS that communicates data.

Figure 1 shows the mapping of the topics to each of their meta-categories. A full view was not readable in Word format due to the large number of topics. As a result, only a section of the full illustration of topics and meta-categories is presented in Figure 1. A full view is available in attachments to this report (see both XMind and image (.png) files of this mapping listed in Appendix 11.1).

f Note that Statistical/Predictive methods and Communications are also each an individual topic under the Technologies meta-category. Each includes all the articles categorized under their respective meta-category. This method for structuring the topic groups allow for the assessment of these two key IMS enabling technologies as a whole and of the sub technologies/specialized techniques under each meta-category.



Figure 1. Mapping of Topics to Meta-Categories



3.3 Concepts & Definitions This section defines key concepts that are prevalent in IMS to provide a framework for the report. But first, it is important to acknowledge that there is great variability and interchangeability in the vocabulary used to describe maintenance approaches and maintenance systems and their components. This is evident in the literature consulted for this study and in previous studies done by the National Research Council of Canada (NRC) on this subject area.9,10 Since the area of “intelligent maintenance” has been studied for many years, the terminology used to describe technologies or approaches has evolved and resulted in the creation of many synonyms or quasi-synonyms. Some approaches or technologies have many synonyms. Some terms refer to IMS for a specific application area (or platform). Furthermore, the same term can be used to describe quite different technologies from one article to another. The most common definitions from recent publications, which follow in this section, are used in this report. Maintenance is an expensive and labour-intensive task to support an item’s/asset’sg lifecycle.11 In the last decades, various approaches have been developed and studied to minimize maintenance burden, increase its efficacy and cost-effectiveness while reducing assets’ down time and unexpected failures. Based on the literature reviewed for this report,1,2,11-22 there are generally three key item/asset maintenance strategies:

1. reactive (or corrective), 2. preventative and, 3. predictive.

The preventative and predictive approaches are described as two types of proactive maintenance. A fourth approach, also proactive, incorporates prognostic maintenance and is the subject of this report: intelligent maintenance (i.e. advanced or integrated predictive/proactive maintenance systems).

Table 3, adapted from the US DoD CBM+ Guidebook, presents the four maintenance strategies which are defined in more details below.

Table 3. Maintenance Strategy

Maintenance approach

Reactive (or Corrective)

Proactive

Preventative Predictive Intelligent (advanced/integrated

predictive)

Type Run-to-failure Fix it before it breaks

If it isn’t broke, don’t fix it

Fix it at the right time

Examples Flat tire Check/add air every month

Check/add air when sensor warning

Check/add air based on data-driven schedule & sensor warning

Pros Machines are not “over maintained”

No condition monitoring costs

Fewer catastrophic failures

Parts are ordered when needed

Life extension Reduced downtime Reduced overall

maintenance costs

g Item: platforms, machines, vehicles, structures, etc.



Greater control over stored parts and costs

Maintenance performed in a controlled manner

Reduced unexpected failure

Maintenance is performed when convenient

Life extension Reduced failure rate

Improved reliability Fewer failures

Cons Unplanned or unscheduled12

High risk of secondary failure

High production downtime

High cost of spare parts

Overtime labour Safety hazards

Repairs are done even when there are not faults

Repair often causes more harm than good

Still unscheduled breakdowns

High investment costs

Additional skills required

High investment costs

Additional skills required

Change in culture Additional upfront

time investment

Source: DoD. Condition Based Maintenance Plus Guidebook, 2008.

Reactive, or corrective, maintenance is carried out following the detection of an anomaly or failure.20 Maintenance is performed after a breakdown, meaning the asset is run-to-failure and there are increased risks of secondary failure as a result. The maintenance is unplanned or unscheduled.12 Preventative maintenance is carried out at predetermined intervals or according to manufacturer requirements or recommendations.20 It can be scheduled or unscheduled (triggered by a signal or a report). It uses the mean time-time-to-failure (MTTF) to schedule upkeep which was proven to be not particularly effective.14 Preventative maintenance is the mainstream and most frequently adopted method20 and while considered proactive is described as a “blindly” proactive approach.2 Predictive maintenance can either use a diagnostic approach (i.e. identify an upcoming failure) or a prognostic approach (forecasting the remaining life).12 It is an approach that examines changes in equipment condition to predict potential failures.14 While preventative maintenance uses a mean-time-to-failure approach, predictive maintenance considers each asset/component individually and extracts metrics that characterize the current health of that individual asset/component to predict when that specific asset/component is likely to fail.1 Maintenance is based on the condition of individual assets. Intelligent Maintenance Systems (IMS) are advanced predictive maintenance systems that integrate both diagnostics and prognostics. They enable a “smart” or “intelligent” approach to identifying, scheduling and performing maintenance tasks based on a more holistic real-time understanding of all critical components in a platform. The condition of individual components are considered collectively in relation to each other in maintenance planning. IMS will be defined more thoroughly below in Section 3.3.1. The subject of this scientometric study is IMS applied to military platforms and related assets. The report focuses on technological advances that enable the advancement of predictive maintenance towards the



implementation of intelligent maintenance. Although corrective and preventative maintenance are out of scope for this report, it is essential to note that all types of maintenance remain necessary for an effective maintenance program.1 Nevertheless, the need for corrective and preventative maintenance is reduced, along with maintenance costs, with a successful intelligent (or advanced predictive) maintenance approach.1 In addition, it is also important to recall that maintenance technologies for industry applications (manufacturing) or infrastructure (utilities, buildings, bridges, roads, tunnels, etc.) are out-of-scope and will not be discussed here, nor included in the analytical dataset. The sub-sections that follow define and describe IMS, and its related or embedded technologies, in more detail to further answer key question 1a.

3.3.1 Intelligent Maintenance Systems (IMS) An Intelligent Maintenance System (IMS) is an advanced predictive maintenance system that enables a “smart” or “intelligent” approach to identifying, scheduling and performing maintenance tasks. An IMS necessarily depends on:3

instrumentation of equipment; continuous monitoring (sensors and other tools) and collection of an asset’s condition

indicators;

predictive analytics on the collected data.

It ideally integrates both diagnostic and prognostic tools to establish a maintenance strategy.h The goal of an IMS is to migrate from traditional to proactive maintenance systems, based on the equipment’s state of operation and degradation.21 An IMS is part of an item’s lifecycle management, also known as maintenance logisticsi or autonomic logistics. It employs continuous or periodic assessments of platform conditions (health assessment monitoring/condition-based monitoring) using sensors or external tests and measurements through first-hand observation or portable equipment. IMS development is driven by advances in the Industrial Internet of Things (IIoT).14 The US Department of Defense (DoD) CBM+ system is an example of a military IMS and is discussed further in Section 4.3. IMS is also known as Predictive Maintenance (PdM)j, advanced Condition-based Maintenance (CBMa), e-maintenance or Artificial Immune Intelligent Maintenance System (AI2MS). IMS is often just referred to as CBMa. In this context, it’s important to remember that a frequently used definition for Condition-based maintenance systems (CBMa) is a maintenance scheme that uses degradation analysis from health assessment and monitoring tools to define the maintenance plan and actions. The goal of CBMa is to perform maintenance only when there is evidence of need.12,23 The term CBMa was first introduced in the late ’40s by the Rio Grande Railway Company and many CBMa concepts and applications have emerged since in various industries.11,19

h Note that some systems only use prognostic approaches. i Other term used: anticipatory logistic, integrated logistics, autonomous logistics, embedded platform logistics systems, etc. j Although Predictive Maintenance is its own category of maintenance, some of the literature continues to use the term Predictive Maintenance even as more advanced/integrated/intelligent features are added to the system that would move the system into the category of IMS. One challenge in writing this report was identifying which approaches were actually intelligent even though they were labeled as something else.



The IMS concept was introduced by J. Lee et al.2 from the National Science Foundation’s Center for Intelligent Maintenance Systems (University of Cincinnati) in 2006 under the term e-maintenance and intelligent prognostics. IMS was defined at the time as systems that enable an established holistic approach to identifying and scheduling maintenance tasks. IMS use data from sophisticated sensors, computerized maintenance tools and monitoring equipment (ex: performance data, warnings, status, etc.) in a practical matter.2 It enables a continuous, seamless and meaningful flow of information throughout the maintenance process. Intelligent prognostics, and thus IMS, is defined as a systematic approach that can continuously track health degradation and extrapolate temporal behaviour of health indicators to predict risks of unacceptable behaviour over time as well as to pinpoint exactly which components of a machine are likely to fail.2 IMS is not a term that is broadly used in the literature. However, because it best describes the type of advanced maintenance systems of interest it is used as the standard term in this study to avoid terminology confusion from variations used in the literature. Intelligent maintenance systems integrate multiple tasks and processes, including Sense & collect, Connect, Store, Analyze and Act as describe in Table 4. Each task/process is supported by many enabling technologies or tools which are often embedded.2,12 Note that not all IMS include all five tasks listed in Table 4 and that these systems vary greatly in scope.14



Table 4. IMS Processes

Actions Technologies/Systems Definition

Sense & collect Sensors & sensing technologies (Data acquisition)

Getting output.

Connect & store System integration & Communications Data recording

Databases; off-board interactive communication links; Open system architecture; integration of maintenance and logistics information systems; interface with operational systems; designing systems that require minimum maintenance; enabling maintenance decisions based on equipment condition. Converts output into a digital parameter.

Analyze Condition-based monitoring (state detection) Diagnostics (Health assessment/monitoring) Prognostics assessment (embedded intelligent prognostics algorithms)

RCM analysis; a balance of corrective, preventative, and predictive maintenance processes; trend-based reliability and process improvements; integrated information systems providing logistics system response; CPI; Serialized Item Management Deals with post-event analysis and includes fault detection, isolation (localization) and identification when it occurs.19 Determine future health state and failure modes based on the current assessment and estimation of future demand/use and remaining useful life.

Act Decision support Automatic decision reasoning/advice generation

Decision support and analysis capabilities, often software, both on and off equipment; appropriate use of diagnostics and prognostics; automated maintenance information generation and retrieval. Provide practical information on maintenance and recommended actions

Coordinate Embedded product services Lifecycle information and management

Closed-loop product design



Figure 2 presents a typical IMS process model.24 Data on the asset/item degradation process is acquired by the sensors/inspections/tests and is transmitted to a host processor or to a data acquisition system integrated in the sensors which normalize the data before transmitting it. Software is then used for data logging, data processing, and data visualization. Based on the algorithm and analysis tool, the software generates an analysis report that is used to make a maintenance decision. The decisions are supported by two main categories of technique: diagnosis and prognosis.25 Diagnosis includes all techniques developed to determine if something is wrong with the equipment, which component is faulty and to determine the nature of such fault.26 Prognosis, on the other hand, covers fault prediction and estimation of time left before an operating failure occurs.

Figure 2. IMS Mode24

IMS helps to resolve two complex operational assets and process problems, as depicted in Table 5.27

Table 5. Problems solved via IMS

Asset Performance Problems Process Performance Problems

Lack of visibility into system health Inability to accurately forecast system

downtime and costs

High costs of unscheduled maintenance High cost of additional systems Impact to Readiness and Mission

Competing maintenance, financial and operational requirements

Lack of visibility of indicators across organizational silos

Inability to leverage analytical insights for process optimization and force readiness



The University of South Carolina Center for Predictive Maintenance, previously known as the Condition based Maintenance Center, is currently developing a Preventive Predictive Maintenance approach that can be considered IMS, presented in Figure 3.3 The centre has developed and successfully demonstrated a number of advanced CBMa projects over the last 15 years.

Figure 3. University of South Carolina's Preventive Predictive Maintenance Approach

New advances in IMS technologies introduced the idea of transforming IMS through the integration of Engineering Immune Systems (EIS) or Artificial Immune Systems (AIS) technologies to achieve self-maintenance. These are known as Artificial Immune Intelligent Maintenance System (AI2MS). EIS and AIS are an emerging branch of artificial intelligence. AIS was introduced in 200828 and are inspired by the biological systems.29 An immune system is a collection of biological processes within an organism that protects against disease by identifying and killing pathogens and tumour cells.29 AIS attempt to copy some functionalities of biological immune systems like feature extraction, pattern recognition, noise reduction, learning memory and function optimization.29 EIS is considered an intelligent system that has the ability to assess and predict system performance and health based on available data using different modelling techniques.30 The focus of EIS is on mechanical engineering systems and on making use of prognostics and health management techniques in order to reach a mechanical system that has self-immunity.29 Integrating EIS/AIS technologies into maintenance systems aims at adding robustness, adaptability (eliminate and/or accommodate faults and failures) and autonomy to these systems. These functionalities are currently lacking in intelligent maintenance systems and in artificial intelligence systems in general.29 Self-maintenance refers to the ability of the assets to itself carry out regular quality and safety checks, to detect anomalies, and to make immediate repairs when needed by using stocked spare parts.29 IMS deployment and implementation involve a wide range of technologies and processes. Lastly, it is important to know that maintenance tasks have technical features and require human interventions.17 IMS includes supporting the actions usually performed by humans through technological advances like artificial intelligence, digitalization, data analytics, forecasting, etc.



3.3.2 Health Assessment Technologies & Condition-Based Monitoring The focus of this report is Intelligent Maintenance Systems (IMS) also referred to as advanced Condition-Based Maintenance (CBMa) systems or advanced Predictive Maintenance (PdM). However, this report also covers new advances for maintenance health assessment tools and maintenance monitoring tools which are essential to these systems. Health assessment and monitoring tools are a key component to IMS. The following section defines related and synonymous technologies. First, the health assessment technology terms are defined. Based on a literature analysis, all of the terms below are virtually synonyms, but they differ in terms of the platforms or systems from which they have evolved or to which they are normally applied.

Integrated Health Monitoring (IHM): The unified capability of systems to assess the current or future state of the member system health.31 Structural Health Monitoring (SHM): Introduced more than 30 years ago, SHM has evolved to include much more than its first definition: “the process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure.”32,33 SHM now includes aspects of diagnosis, prognosis and decision support34 which correspond to the IMS definition used for this report.

Health and Usage Management Systems (HUMS): A generic term given to activities that utilize data collection and analysis techniques to help ensure availability, reliability and safety of vehicles.35 This term is often used in reference to airborne craft and rotorcraft in particular. It was introduced more than 30 years ago, but also continues to evolve.

Integrated Vehicle Health Management (IVHM): The unified capability of systems to assess the current or future state of the member system health and integrate that picture of system health within a framework of available resources and operational demand.31 It has evolved from maintenance work with fixed wing aircraft to become a standard term for the maintenance of ground vehicles.36

Prognostic and Health Management (PHM): Links failure mechanisms to system lifecycle management. It uses information to allow early detection of impending or incipient faults, remaining useful life calculations, and logistical decision-making based on predictions.37 It is often used for aircrafts and engines.

Airplane Health Management (AHM): A management approach focused on improving operational performance by turning available airplane data into useful, actionable information.38

Integrated Condition Assessment Systems (ICAS): A system-level decision process of integrating data for condition-based assessment and in managing both aleatory and epistemic uncertainty in the system.39

Second, Condition-based Monitoring (CBMo) and Reliability-centred Maintenance (RCM) are defined below.

Condition-based monitoring (CBMo): Monitoring of machinery to look for significant changes that could be indicative of a problem within the machinery. It’s mainly done by sensors and actuators, but it can be done by inspection as well. It can also be seen as the link between the sensor systems and the health assessment tool. New sensor technologies like bio-inspired sensors, innovative sensor network technologies, embedded self-sensing and self-diagnostic



capabilities are a current trend. It’s also known as Integrated. There are two types of condition monitoring (continuous and periodic) and two limitations to continuous monitoring(costs and inaccurate diagnostics).19 The pitfalls of periodic monitoring include the possibility of missing failure events that occur between inspections and determining the right condition monitoring. However, periodic monitoring is often preferred because it provides an accurate diagnosis using filtered and processed data.19

Reliability-centred Maintenance (RCM) (also Reliability Availability Maintenance (RAM)): RCM is a logical, structured process used to determine the optimal failure management strategies for any system, based on system reliability characteristics and the intended operating context. It was introduced in the early ’70s by the aerospace industry when it was determined that the cost of applying standards to new aircrafts, such as Boeing 747, would make them uneconomical to operate.40 It can also be defined as a method for developing and selecting maintenance strategies and design alternatives, based on operational, economic and safety/environmental criteria.19 RCM defines what must be done to a system to achieve the desired levels of safety, reliability, environmental soundness, and operational readiness, at the best cost. RCM is intended to be applied continuously throughout the lifecycle of any system.41 Others defined RCM as a maintenance strategy that selects the right balance between corrective, preventative and predictive maintenance.11 CBMa can include an RCM system. Usually, RCMs are applied to the most critical assets. Reliability-centred Maintenance (RCM) is a related group of technologies that is sometimes embedded in IMS. The following statistical methods are commonly used RCM analysis: Experimental Modal Analysis (EMA), Failure Modes and Effects Analysis (FMEA), Planned Maintenance Optimization (PMO), Event/Fault trees, Root Cause Analysis (RCA), Why analysis (Toyota method), Practical Problem Solving (PPS), Cause and effect diagrams (Ishikawa), SCRA (Symptom, Cause, Remedy, Action), Six Sigma, Pareto analysis, failure reporting, supportability analysis, maintainability demonstration (M-Demo), Logistics Product Data, Integrated Support Product Element, Maintenance Task Analysis (MTA) are types of maintenance analysis done in RCM.42,43



4 SIGNIFICANT POLICIES, STRATEGIES AND ROADMAPS The following section identifies key recent standards, policies, guidance documents and roadmaps for IMS military platforms from the selected allied countries (key question 1b). The documents were gathered through the literature search done for this project and through extensive Web browsing of allied countries’ online information sources including ASSISTk, SAE and the NATO Standards database. Priority was placed on documents with a holistic approach to IMS and related technologies versus documents related to a specific component or condition within a platform. There are many documents on the latter, but these are too specialized for the purpose of this section. One example frequently quoted in the IMS literature is the NATO CBMo procedures for aircrafts on vibration control.8 Communications related standards were addressed in Section 7.4 and thus excluded here. The majority of documents refer to IMS subgroups of technologies (ex. Structural Health Assessment) and are intended for Aerospace platforms. Most of the policies and roadmaps identified were from the US DoD. The full list of documents is available in attachment see Appendix 11.1. Earlier documents can also be found in the 2012 NRC scientometric study Condition Based Maintenance: A Literature Survey10 created for DRDC.

An important finding is that military platform maintenance has been increasingly outsourced.8

Documentation related to maintenance managed by private corporations is usually not publicly available

and cannot be included in this report. In addition, there are nearly as many outsourcing formats and

implementation strategies as there are contracts. Examples of projects led by private companies for the

military sector are shown in Table 6.

Table 6. Examples of Military Maintenance Projects Led by the Private Sector

Organization Project/Client Product name/program

CDM Smith The Naval Facilities Engineering Command (NAVFAC), the US Navy’s utility company

Smooth Sailing for Navy’s Asset Management Program

QinetiQ Australia Australian Department of Defence

Asset Management “From creating an Asset Management model for the acquisition of new amphibious assault ship capability to making recommendations on how to extend the life of an ageing fleet of strike fighter jets, our Asset Management services are

k ASSIST is a military specifications and standards database indexing US Defence Standardization Program (DSP) documentsk. Although, the assistdocs.com website has been discontinued, the documents cleared for public release are still accessible from ASSIST Quick Search web page.

https://www.cdmsmith.com/en/Client%20Solutions/Projects/Navy%20Asset%20Management%20Programhttps://www.cdmsmith.com/en/Client%20Solutions/Projects/Navy%20Asset%20Management%20Programhttps://www.cdmsmith.com/en/Client%20Solutions/Projects/Navy%20Asset%20Management%20Programhttps://www.qinetiq.com.au/services/advice/asset-management.aspxhttp://quicksearch.dla.mil/qsSearch.aspx



Organization Project/Client Product name/program

adapted to suit the needs of our clients.”

GE US Navy’s Military Sealift Command

Health and usage monitoring system (SIE/HUMS)

General Dynamics UK UK Ministry of Defence (MoD)

Predix Asset Performance Management (APM)

OutSystems - Atos Royal Navies of Australia, New Zealand, Malaysia, and Oman

AMPS (“The Move to Modernize a Legacy Platform”)

Siemens Royal New Zealand Navy SISHIP and SINAVY Life Cycle Management (LCM); (MEKO-200 ANZAC frigates Project: modernization of the integrated platform management system (IPMS)

Mikros Systems Corporation

US Navy ADEPT Distance Support Sensor Suite (ADSS) for the LITTORAL COMBAT SHIP (LCS)

4.1 IMS Dataset In the IMS dataset collected for this report, there are 230 publications (5%) out of 4,753 publications that could be considered roadmaps, policies, strategies, frameworks, guidelines or guidance publications or that are discussing the requirements to establish such documents. After validation, 60 (26%) out of these 230 publications are military focused or discuss military applications. This includes 29 for Aerospace platforms, 22 on Navy/Maritime platforms and five on land platforms (see Figure 4). Interestingly, five documents pertain to cybersecurity recommendations for IMS.

Figure 4. # Publications by Document Type and Platform, Limited to the Military Datasetl

l Note that the aerospace, automotive and navy/maritime platforms are limited to military-related content only and are not mutually exclusive as well as the document type categories.

https://vpoglobal.com/2018/02/07/ge-build-digital-twin-military-sealift-command-improve-vessel-performance/https://vpoglobal.com/2018/02/07/ge-build-digital-twin-military-sealift-command-improve-vessel-performance/https://vpoglobal.com/2018/02/07/ge-build-digital-twin-military-sealift-command-improve-vessel-performance/http://www.army-technology.com/news/newsgd-supply-first-health-usage-monitoring-system-british-army/http://www.army-technology.com/news/newsgd-supply-first-health-usage-monitoring-system-british-army/https://www.outsystems.com/case-studies/atos/asset-management-system/https://www.outsystems.com/case-studies/atos/asset-management-system/https://www.outsystems.com/case-studies/atos/asset-management-system/https://www.industry.siemens.com/verticals/global/de/marine/handelsschiffe/service-schiffbau/Documents/siship-sinavy-lcm-en.pdfhttps://www.industry.siemens.com/verticals/global/de/marine/handelsschiffe/service-schiffbau/Documents/siship-sinavy-lcm-en.pdf



Figure 5 presents the related military publications discussed above by document type and by country (author affiliation). Countries marked by an asterisk are allied countries. Out of the 60 military publications, 26 (43%) are authored by at least one author from an allied country. China is the country with the most publications (24 documents).

Figure 5. # Publications by Country and Document Type, Limited to the Military Datasetm

Many private companies published white papers and/or roadmaps on brand name technologies or specific vehicles which were not included in the scope of this study.

4.2 Standards 4.2.1 SAE The literature points towards the SAE standards as a useful source of information regarding certification, standards, regulations and policies for the military sector, especially for NATO members. Below is a list of select relevant standards identified via a search of SAE.n SAE has a CBM (Condition Based Management) Subcommittee under the Ground Vehicle Reliability Committee, but no public document is available at this time.

m Document types and country affiliations are not mutually exclusive. n Note that NRC is not a SAE members, thus access was limited to the list of standards. NRC does not have access to the full text of the standards and therefore cannot extract further detail. If it is necessary to get the full text, the client will have to be purchase.

https://www.sae.org/servlets/works/committeeHome.do?comtID=TEMGVR4https://www.sae.org/servlets/works/committeeHome.do?comtID=TEMGVR4



General Condition Based Maintenance (CBM) Recommended Practiceso 2012-01-17 WIP Standard: ARP6204 The scope of this document is to clearly lay out the path for an organization to implement a CBM approach to maintenance. The practices will include both CBM in design and in the support phase for fielded equipment. Aerospace Diagnostic and Prognostic Metrics for Engine Health Management Systems 2017-01-24 WIP: AIR5909 This SAE Aerospace Information Report (AIR) presents metrics for assessing the performance of diagnostic and prognostic algorithms applied to Engine Health Management (EHM) functions. This document consolidates and expands upon the metric information previously contained in AIR4985 and AIR5909. The emphasis is entirely on metrics and as such is intended to provide an extension and complement to such documents as ARP4176, which provides insight into how to create a cost-benefit analysis to determine the justification for implementing an EHM system. IVHM Concepts, Technology and Implementation Overview 2016-03-16 ARP6803 This SAE Aerospace Recommended Practice (ARP) examines a comprehensive construct of an Integrated Vehicle Health Management (IVHM) capability. This document provides a top-level view of the concepts, technology, and implementation practises associated with IVHM. This keystone document of the SAE HM-1 Committee is not intended as a legal document and does not provide detailed implementation steps, but does address general implementation concerns and potential benefits. Applicable Integrated Vehicle Health Monitoring (IVHM) Regulations, Policy, and Guidance Documents 2016-01-03 WIP: AIR6900 This AIR will address the need for a strategy to achieve aircraft operating certificate holder maintenance efficiencies within the existing regulatory environment as well as the need for regulation, policy, and guidance changes in the long-term to accommodate more complex IVHM solutions. This document will analyze which IVHM solutions can be incorporated within existing maintenance procedures and which also comply with regulations, policy, and guidance. One of the AIR’s objectives is to define best practices for aircraft operating certificate holders to engage with regulators to get approval for simpler IVHM applications leading to maintenance efficiencies. Additionally, this document will analyze the barriers that existing regulations, policy, and guidance present to the implementation of more advanced IVHM solutions. The result is a set of recommendations to certify and implement end-to-end IVHM solutions for the purpose of gaining maintenance efficiencies.

o Google web cache link is provided since direct link in SAE database is not accessible for NRC-KITS.

https://webcache.googleusercontent.com/search?q=cache:agdpqQpu0mIJ:https://www.sae.org/standards/content/arp6204/+&cd=1&hl=en&ct=clnk&gl=cahttps://www.sae.org/servlets/works/documentHome.do?docID=AIR7999&inputPage=wIpSdOcDeTaIlS&comtID=TEAE32https://www.sae.org/servlets/works/documentHome.do?comtID=TEAHM1&docID=ARP6803&inputPage=dOcDeTaIlShttps://www.sae.org/servlets/works/documentHome.do?docID=AIR6900&inputPage=wIpSdOcDeTaIlS&comtID=TEAHM1



Guidelines for Implementation of Structural Health Monitoring on Rotorcraft 2015-04-28 WIP Standard: AIR6892 A Guide to Aircraft Power Train Monitoring 2014-04-14 WIP Standard: AIR4174A Aircraft Gas Turbine Engine Health Management System Development and Integration Guide 2014-02-21 WIP Standard: ARP5120 Guidelines for Implementation of Structural Health Monitoring on Fixed Wing Aircraft 2013-09-19 Standard: ARP6461 Guidance on the evolution and integration of Structural Health Monitoring systems for military aircraft 2013-03-25 WIP Standard: ARP6245 Health and Usage Monitoring Metrics, Monitoring the Monitor 2012-10-24 ARP5783

4.2.2 NATO The standards listed in Table 7, in chronological order, were identified using the publicly available promulgated standard database from NATO. They mainly relate to armaments and munitions, not vehicles.

Title/Link Document Number Year

NATO Guidance On Integrated Logistics Support For Multinational Armament Programs

ALP-10 Ed. C Ver. 1

2017

In-Service Surveillance Of Munitions Condition Monitoring Of Energetic Materials

AOP-64 Ed. A Ver. 1 2017

International Specification For Developing And Continuously Improving Preventive Maintenance

NNSTD ASD S4000P 2014

International Procedure Specifications For Logistic Support Analysis NNSTD ASD AIA S3000L 2014

Guidance For Managing Dependability In-Service ADMP-02 2014

Allied Joint Doctrine For Modes Of Multinational Logistic Support AJP-4.9 Ed. A 2013 Table 7. Identified Relevant NATO Standards

http://standards.sae.org/wip/air6892/http://standards.sae.org/wip/air4174a/http://standards.sae.org/wip/arp5120/http://standards.sae.org/arp6461/http://standards.sae.org/wip/arp6245/http://standards.sae.org/wip/arp6245/https://saemobilus.sae.org/content/arp5783https://nso.nato.int/nso/zPublic/ap/PROM/ALP-10%20EDC%20V1%20E.pdfhttps://nso.nato.int/nso/zPublic/ap/PROM/ALP-10%20EDC%20V1%20E.pdfhttps://nso.nato.int/nso/zPublic/ap/PROM/AOP-64%20EDA%20V1%20E.pdfhttps://nso.nato.int/nso/zPublic/ap/PROM/AOP-64%20EDA%20V1%20E.pdfhttps://nso.nato.int/nso/zPublic/nnstd/ASD%20S4000P.pdfhttps://nso.nato.int/nso/zPublic/nnstd/ASD%20S4000P.pdfhttps://nso.nato.int/nso/zPublic/nnstd/ASD%20AIA%20S3000L.pdfhttps://nso.nato.int/nso/nsdd/apdetails.html?APNo=1868&LA=ENhttps://nso.nato.int/nso/nsdd/apdetails.html?APNo=1544&LA=FR



4.3 US NASA was the first DoD agency to launch a predictive maintenance program in 1999. The NASA LBS program followed by NASA BOSC in 2002.44 Likewise, DoD launched the CBM+ initiative in 2002 which was followed by a CBM+ policy in 2007 and a CBM+ guidebook23 in 2008 managed by the CBM+ Action Group which includes members from all military agencies (Army, Navy, Air Forces).45 The CBM+ program is driven by DoD and Army policies, including “DoD Instruction 4151.22: CBM+ for Materiel Maintenance”, “Army Regulation (AR) 70-1”, “Army Acquisition Policy”, “AR 750-1 Army Materiel Maintenance Policy” and “AR 700-127, Integrated Logistics Support”.27,43,45 The CBM+ program was initiated by the US Army Communications-Electronics Common Logistics and Readiness Center (CECOM LRC) in 2008 on the C4ISRp legacy systems.27 The CBM+ is part of an integrated and collaborative effort with partners from:

CECOM Software Engineering Center Army Logistics Innovation Agency (LIA) Logistics Support Activity Communications-Electronic Research, Development and Engineering Center (CERDEC) Tobyhanna, Letterkenny, and Sierra Army Depots Aberdeen Test Center Aviation and Missile Research Development and Engineering Center Army Materiel Systems Analysis Activity

In 2014, these agencies had already completed an early operational assessment of CBM+ technology both on tactical quiet generators (TQGs) and the C4ISR command post platform to validate a proof of concept model. The assessment also provided successful demonstrations of CBM+ data collection and transmission strategies across the Common CBM+ Architecture currently under development within DoD CERDEC. In 2014, CECOM LRC planned to evaluate 14 C4ISR systems (list not available).27 DoD CBM+ comprises a more advanced proactive and predictive approach than CBMa and is driven by condition sensing and integrated analysis-based decisions. It also includes reliability centred maintenance systems at its core. The DoD CBM+ definition is:

CBM+ is the application and integration of appropriate processes, technologies, and knowledge-based capabilities to achieve the target availability, reliability, and operation and support costs of DoD systems and components across their lifecycle. At its core, CBM+ is maintenance performed based on evidence of need, integrating reliability centred maintenance (RCM) analysis with those enabling processes, technologies, and capabilities that enhance the readiness and maintenance effectiveness of DoD systems and components. CBM+ uses a systems engineering approach to collect data, enable analysis, and support the decision-making processes for system acquisition, modernization, sustainment, and operations.41 CBM+ is also a set of practices that builds on CBM by leveraging real-time data collection, transformation, and analysis to inform and augment maintenance activities.46

p Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Systems



Figure 6 presents the DoD CBM+ model, illustrating the various components, systems and processes that are required for CBM+.

Figure 6. DoD Condition Based Maintenance Plus Model41

DoD Office of the Assistant Secretary of Defense for Logistics & Material Readiness maintains a Website

on CBM+ with the most recent plans and policies. The US DoD’s most recent CBM+ plan is from 201345

and lists a selection of what were current DoD CMB+ projects at that time (Table 8).47

Table 8. US DoD CMB+ Projectsq

Platform Project Type Project Name

Air Force New acquisitions C-17 CBM+ Pilot Program

Air Force New acquisitions JSF Prognostic Health Management

Air Force New acquisitions F-22 Integrity Data and Reporting System (IDARS)

Air Force Legacy systems C-130 Automated Inspection, Repair, Corrosion and Aircraft Tracking (AIRCAT)

Air Force Legacy systems C-5 Aging Fleet Integrity Management (AFIRM)

Air Force Legacy systems Engine Health Management Plus (EHM+)

Air Force Legacy systems Apache CBM+ effort

Air Force Programs C5 MSG-3

Air Force Programs C-130 and B-1 HVM Programs

Air Force Programs Collective Mind Predictive Trending

Air Force Programs CBM+ Research Environment and Web Services

Air Force Programs Systems Lifecycle Integrity Management (SLIM) AFIT Educational Course

Air Force NA F-15 RCM Analysis Preventive Maintenance Package

q Not exhaustive.

https://www.acq.osd.mil/log/mpp/cbm+.htmlhttps://www.acq.osd.mil/log/mpp/cbm+.html



Platform Project Type Project Name

Air Force NA E-8C (JSTARS) Preventive Maintenance Package

Air Force NA USAF KC10 (2008)

Air Force NA USAF C-20 (2009)

Air Force NA USAF C-21 (2013)

Air Force NA DOS Air Wing (2014)

Army NA Army TH67 (2012)

Army AMRDEC Aviation System Assessment Program (ASAP)

Army Partnership TACOM, a RCM platform (a partnership with Penn State ARL and Control Point Corporation)

Marines NA NET Centric Integrated Logistics (Anticipatory Logistics, Enterprise Health Management, Failure Management Strategies, Reliability Availability Maintainability)

Marines NA USMC Condition-Based Maintenance (CBM+) Enterprise Implementation Initiative – Phase III

Marines/Army NA Joint Light Tactical Vehicle (JLTV)

NA NA Fleet Maintenance Effectiveness Reviews

NA NA MIL-STD 3034 Reliability Centered Maintenance Processr

NA NA LOGCAP (2006)

NA NA NRLN/CoBRA (2007)

NA NA US CG LIMS (2011)

NAVAIR, SAS Institute, National Center for Manufacturing Sciences (NCMS)

NA Enterprise Condition-Based Maintenance Plus (eCBM+) Interoperability – Phase II

Navy NA Naval Sea Systems Command (NAVSEA) CBM+ Strategy

Navy NA Littoral Combat Ship (LCS) CBM+ pilot

Navy NA Add automatic analytic and remote monitoring systems to SKED (PMS scheduling tool)

Navy NA Integrated Condition Assessment System

Navy NA NAVAIR 0-25-403

In November 2017, a news story on the National Centre for Manufacturing Sciences website mentioned that it is currently supporting the US Naval Air Systems Command (NAVAIR) in par

Scientometric Study on Intelligent Maintenance Systems for … · 2018. 7. 17. · Scientometric...

Documents

Transcript of Scientometric Study on Intelligent Maintenance Systems for … · 2018. 7. 17. · Scientometric...