Expanding Applications, Data Management Technologies and ... · Expanding Applications, Data...

AIAC12 Twelfth Australian International Aerospace Congress19 – 22 March 2007

Expanding Applications, Data Management Technologies andBenefits of HUMS

Steve BoakesSmiths Aerospace

Eastleigh, Hampshire, SO53 4YG, United Kingdom

AbstractThe primary motivation for installing Health & Usage Monitoring Systems (HUMS) in the early 1990swas the desire to enhance the safety of helicopter operations. Since that time, significant benefits tomaintenance and operations have been realised and HUMS is now more widely seen as a worthyinvestment to new and aging, civil and military platforms of all kinds. HUMS has now been applied to,and will continue to be developed for, wider applications such as fixed wing aircraft and land vehicles.This range of applications, their different environments, cultures and capabilities is demanding thatsuppliers of HUMS technology develop HUMS data acquisition, processing, data analysis andmanagement components and tools to support multiple applications.

Smiths first developed and supplied HUMS for helicopters and is now using its world leading experienceand capabilities to develop HUMS and evolve Integrated Vehicle Health Management (IVHM) andPrognostic Health Management (PHM) systems for a range of vehicle types.

This paper recounts the growth of helicopter HUMS, the evolution of HUMS and IVHM for newapplications and the pursuit of further benefits. It then identifies related challenges and outlines ways inwhich Smiths Aerospace is developing its existing and new diagnostic, prognostic and data managementcapabilities to ensure that maximum benefits are realised by a wider range of users.

1. ESTABLISHED BENEFITS ANDGROWTH OF HELICOPTER HUMS

The introduction of the first HUMS in the early1990s was driven by the desire, and arguably anethical obligation, to improve the safety of NorthSea helicopter operations. For some time,evidence had suggested that vibration-baseddrivetrain monitoring could provide early andaccurate detection of incipient mechanical defects.First generation HUMS boasted advancedvibration-based drivetrain diagnostics andautomatic exceedance monitoring. By the end ofthe 1990s, the UK Civil Aviation Authority(CAA) claimed that “HUMS…has alreadyreduced fatal accident statistics” and SINTEF [1]concluded that “HUMS was probably the mostsignificant isolated safety improvement of the lastdecade”. It is arguably the requirement forincreased safety that has resulted in the vastmajority of HUMS installations to date, wherecivil operators are increasingly required to meet

related regulatory requirements and more militaryoperators are acknowledging a ‘duty of care’.

The implementation of HUMS and many of itsmatured functions was also driven by themotivation to save costs through maintenance andoperational benefits. Many such benefits are nowreasonably established and well documented [2],[3]. Whilst the timely and accurate detection ofhealth defects remained a key capability, theemphasis on accurate monitoring andmanagement of aircraft and component usagegrew rapidly. It was quickly realised that theefficient combination of these capabilities couldsignificantly enhance fleet management andachieve the broad benefits of increasedavailability and reliability, reduced downtime andthrough life costs. For many HUMS operatorstoday a simple, non-exhaustive summary of morespecific benefits is likely to include many of thefollowing:

Improved maintenance planning & targeting

5th DSTO International Conference on Health & Usage Monitoring1


Reduced dedicated test flights Reduced unscheduled maintenance Reduced nugatory maintenance, e.g. No Fault

Found (NFF) Time Between Overhaul (TBO) and inspection

period extensions Component life extensions Reduced logistics (e.g. spares) footprint Improved component Mean Time Between

Failure (MTBF) More efficient aircraft usage Improved aircraft and component reliability Improved incident investigation

The growth in the awareness of such benefits hasresulted in increasing demand for HelicopterHUMS from both civil and military operationsinternationally. Procurement is being increasinglyjustified by anticipated through life cost savingsafforded by comprehensive HUMS functionality.Indeed, within the last year, Smiths has seenexamples of continued growth and diversificationin terms of demand for its more comprehensiveHUMS: the UK MoD contract for thedevelopment and supply of GenHUMS (combinedHUMS/CVFDR) for 70 new AgustaWestlandFuture Lynx Helicopters; the Republic of SouthKorea contract for the localised development ofthe Korean Utility Helicopter (KUH) HUMS,leading to 245 production systems; the 100th

HUMS order for the new civil AW139.

2. EVOLUTION OF HUMS AND IVHMFOR FIXED-WING AIRCRAFT

AND LAND VEHICLES

There is now compelling evidence that traditionalhelicopter HUMS and other evolving prognostichealth management (PHM) technologies are beingspecified, developed and applied for military andcivil fixed-wing aircraft and, more recently, landvehicles. In many cases, modern aircraft/vehicleelectronics architectures and new and networkedlogistics systems are also opening the door tointegrate health monitoring and managementsystems with on-board and ground based systemsand processes, giving rise to the concept ofIntegrated Vehicle Health Management (IVHM).

As the most obvious example of a new and majormilitary fixed-wing programme, the Joint StrikeFighter (JSF) programme is one of the first toinvest in a comprehensive and integrated healthmanagement capability. The recognisedrequirement for maximised mission readiness andavailability has led to the specification anddevelopment of a powerful Force LifeManagement (FLM) system. The aircraft itselfwill be equipped with a combination of traditionaland advanced sensors. The ground based FLMsystem will perform advanced diagnostics andprognostics, individual aircraft and fleetwide lifetracking, detailed engineering analysis andintegrate with the JSF Autonomic LogisticsInformation System (ALIS).

The growing appreciation of maintenance andoperational benefits through health managementhas sponsored a number of initiatives to bettermanage the life of both new and aging legacyfleets of military fast jets and transport aircraft.There are also a number of initiatives to applyintelligent health monitoring to Unmanned AirVehicles (UAVs) where there is no crew toidentify and report faults, take evasive action orhelp provide input to assist correctivemaintenance action.

In the highly competitive world of commercialairlines, where unscheduled downtime carriessuch explicit costs and huge penalties, airframerssuch as Airbus and Boeing now offerperformance-based through life maintenancesupport services and are taking a more proactiveapproach to health management. Motivationsinclude minimised inventory and logistics costsfor the airframer whilst the airline benefits frompredictable support costs, optimized aircraftavailability and maximized ‘on-wing’ componentlife. An important enabler is that new airlinerdesigns now include Integrated Modular Avionics(IMA) architectures. These provide a commonplatform on which multiple applications can berun in parallel and thereby facilitate a trulyintegrated health management approach andmaintenance infrastructure.

Commercial land vehicles and notably,commercial rail vehicles, are increasingly thesubject of initiatives to improve safety and reducetheir traditionally high operating and maintenance



costs. In the UK, a series of high profile accidentsduring the latter 1990s, sponsored the introductionand eventual mandatory requirement for fleetwideinstallations of Operational Train MonitoringRecorders (OTMRs) to monitor how the vehicleswere being operated. Today, a number of leasingcompanies and operators are pioneering theintroduction of permanent, and in some casesintegrated health management technology, someof which is derived from corresponding initiativesin aerospace.

IVHM is now being focused on military landvehicles. The US Future Combat System (FCS)family of military vehicles variants will includecomprehensive health and usage monitoringcapability. In the UK, the maturing and majorrequirement for the Future Rapid Effect System(FRES) and corresponding drive to ensure amodern capability with minimised Whole LifeCosts (WLCs) has been the catalyst for anintegrated electronics architecture that includes anend-to-end (E2E) HUMS capability. The draftrequirement for such an E2E HUMS was capturedin DefStan 25-24 [4] which was an importantinput to the two parallel and competitiveElectronics Architecture (EA) TechnologyDemonstrator Programmes (TDPs) that concludeearly this year.

Notably, the evolution of modern on-boardintegrated electronics architectures such asaircraft Integrated Modular Avionics (IMA) andland vehicle vetronics are now assisting theintegration of health management capabilities onnew aircraft and vehicle designs. Traditionally, onlegacy platforms where data is acquired on-board,the data is communicated and stored by dedicatedconnections and storage media for post-flight/mission analysis by independent ground-based systems. This involves greater weight andcomplexity on-board whilst data analysis on theground can be a disparate process. Modern on-board systems, however, provide a modularcomputing hardware platform and partitionedoperating system that hosts all the software forboth the mission and utility functions. Most, if notall data is inherently available and multipleapplications can be run in parallel. As such, theintegrated on-board electronics system provides acommon physical and logical maintenance

infrastructure that facilitates a truly integratedhealth management approach.

3. EXPANDING BENEFITS - CONDITION BASED MAINTENANCE AND

PERFORMANCE BASED LOGISTICS

Two modern, separate but potentially linked,initiatives that promise to further increasemaintenance and operational benefits areCondition Based Maintenance (CBM) and therecent growth in Performance Based Logistics(PBL) contracts.

There is an increasing desire to use establishedHUMS and evolving IVHM technology totransition from the traditional maintenanceapproach to one that includes CBM. On HUMS-equipped helicopters in particular, a number ofmilitary operators have already granted servicingperiod extensions for specific drivetraincomponents. However, for civil operations, thechange to CBM requires the award of HUMS‘maintenance credits’ in accordance withAdvisory Circular AC 29-2C MG-15 [5], wherebyassurance is obtained that the HUMS will reliablydetect any failure modes that are currentlycontrolled through scheduled maintenance andcan initiate timely maintenance intervention. Dueto the challenging certification processes, HUMScivil in-service experience demonstrates theaward of very few maintenance credits.

Recent FAA sponsored research by Smiths andthe Sikorsky Aircraft Corporation suggests,however, that there are good CBM candidates,particularly where there are a limited number ofpotential failure modes and in-service evidencethat HUMS monitoring systems can reliablydetect such failure modes [6]. In these cases,CBM benefits are likely to include extension oreven elimination of time-based inspection andservicing periods resulting in reducedmaintenance man-hours and aircraft downtime.The credit-potential of HUMS will almostcertainly increase as more in-service experience isgained. However, whilst the mature HUMSdiagnostic technologies provide the original basisfor a CBM approach, evidence suggests that it islikely that the combination of these with evolvingintelligent decision support technologies will



accelerate the CBM credit validation process forcivil applications and ultimately contribute to theimplementation of CBM across a range of bothcivil and military applications.

A number of parallel initiatives are underway toaccelerate the inclusion of CBM for militaryaircraft. One of these is the development andpublication of open standards for IVHM systems,led by the publishing of the Open SystemsArchitecture for Condition Based Maintenance(OSA-CBM) standard on new aircraft and vehicledesigns. OSA-CBM defines a common structurethat enables multiple companies to work togetherto produce the software components for anoptimised IVHM system where all the data isavailable to the operator in a single location andformat. The standard was developed under aNAVAIR Dual Use Science and Technology(DUS&T) programme in 2002 and is now beingused on a range of IVHM initiatives andprogrammes, some of which are described later inthis paper.

Offboard Processing

Onboard Processing

#7 PRESENTATION

#6 ADVISORY GENERATION

#5 PROGNOSTICS

#4 HEALTH ASSESSMENT

#3 STATE DETECTION

#2 DATA MANIPULATION

#1 DATA ACQUISITION

Presentation layer is the MMI

AG utilizes spares, logistics, manning to optimise maintenance operations

Prognostics aims to predict future health trends and provides certainty levels

HA determines current health.

SD compares data to specific features and determines state

DM does the initial signal processing and data reduction

Data Acquisition is sensor and / or A/D conversion

Figure 1: OSA-CBM Structure

Recent years have seen a rapid growth in PBL andPower-By-the-Hour (PBH) contracts where theoriginal equipment supplier (OEM) or anapproved service organisation takes on thesupport of the equipment (engines, transmission,or entire aircraft) and guarantees levels ofavailability of aircraft systems, spare parts andmaintenance. Such contracts offer benefits toboth the OEM/service provider and operator. Theoperator is provided with a single cost-effectivesupport solution with predictable support costs,optimised aircraft despatch reliability and high-quality maintenance. The OEM/service providercan minimize inventory and logistics costs andallow operators to focus on keeping parts “onwing” longer. It is likely that the predictive healthmonitoring capability that supports a CBM

approach will play an increasingly significant rolein such contracts.

4. CHALLENGES FOR TOMORROW’SHUMS/IVHM SUPPLIERS

The rapid expansion of health managementapplications and range of users is forcingsuppliers to both adapt existing technologies anddevelop new approaches to meet the requirementsof different environments, operating andmaintenance cultures and operator skill levels.

Suppliers of HUMS and IVHM technologies arenow challenged, more than ever, to providegeneric, flexible, expandable and cost-effectivecapabilities that can simultaneously providethrough life benefits to a wider range ofapplications and users. Whilst the adage ‘the rightinformation to the right place at the right time’still holds, specific challenges include:

Effective and efficient use of simple andtraditional, new and advanced sensing andmeasurement techniques and technologies.

On-board acquisition, processing and analysisapplications that can be implemented either aspart of a standalone system or embedded into avehicle’s existing integrated electronicsarchitecture.

More efficient data transfer.

Automatic and intelligent data integrityvalidation.

User-configurable systems and tools that donot require software skills.

Open, expandable and interoperable systemsthat can interface to and integrate with 3rd-party diagnostic, prognostic and logisticssystems.

Remote access to information includingautonomous vehicle monitoring and analysis.

More concise presentation of advanceddiagnostic, prognostic and life managementinformation.

Intelligent decision support tools can enhanceanomaly detection capabilities, diagnostics andprognostics and support a CBM approach.



More efficient and creative use of existing datato help meet the weight, space, environmentalconstraints of a specific application.

Capability and flexibility to provide benefitsfrom early stages of implementation.

5. SMITHS ACTIVITIES

Smiths has developed and continues to developsystems, techniques and technologies and supportprogrammes and initiatives that bring benefits to awider range of applications and users.

The following sections illustrate examples ofSmiths systems, tools, research anddemonstrations and development and productionprogrammes that directly address many of thechallenges associated with expanding applicationsand benefits.

5.1 Fleet & Usage Management System(FUMSTM)

Since the early 1990s, Smiths has worked closelywith the MoD to evolve the Fleet & UsageManagement System (FUMSTM) [7], [8].FUMSTM provides a single, modular softwareframework that can host a wealth of built-indecision support tools and will interface to 3rd

party applications and systems, as illustrated inFigure 2.

DAPAT

UserModules

NeuralNetwork

s

Statistical Analyses

A/C-EngAlgorithms

BayesianAnalyses

Deeper & Automatic Trending (Data Mining)Threshold Setting ToolsVisual Flight ReplayUsagePrognostics

Rotor Track & Balance

Flight Data Recorder Data

Instrumented Aircraft Data

Oil Wear Debris

Database

Aircraft & engineHUMS

DatabasesExternalAnalysisPackages

Logistics / Maintenance

SystemsUnscheduledMaintenance

Database

FUMS

StripReports

Engine Vibration

Figure 2: FUMSTM

FUMSTM also allows users to build their preferredapplications without software rewriting, as shownin Figure 3.

Figure 3: Indicative User-Generated FUMSTM

Applications

As such, FUMSTM has been configured andutilised by military users to manage structural,engine and avionics usage for a wide range ofrotary-wing and fixed-wing aircraft fleets.

5.1.1 Diagnostic/Prognostic Tools

The user is able to access and exploit acomprehensive built-in toolset but also plug-inthird-party tools and algorithms. Equally, theFUMSTM graphical and analysis capabilitiesfacilitate display and process data from externalsources. The built-in toolset includes signal-processing, statistical and mathematical analysistools, a range of AI-based diagnostic, prognostic,life management and decision support tools and asuite of proprietary specialised tools such as flightrecreation, flight envelope exceedance andautomatic trending. The FUMSTM tools addressmany of the challenges previously listed and canbe applied to a wide range of platforms andapplications. By way of example, the UsageIndices, data integrity validation and ‘virtualsensor’ specialised tools are summarised below.

‘Usage Indices’The Smiths FUMSTM Usage Indices (UIs)provide concise summaries of recorded flightdata and, at the same time, indicate the impactof usage on component condition and life.Developed and implemented to supportOperational Loads Monitoring (OLM) andStructural Prognostics Health Management(SPHM) on UK military aircraft, UIs havebeen demonstrated to accurately compute thefatigue of engine and structural components.UIs can also summarise sensor data, strain



data, vibration data and other data derivedfrom measured flight data to provide furtherprognostic information to evaluate thecondition/life of additional aircraft subsystemsincluding electronic equipment. UIs provide ahigh data compression ratio without loss ofsignificant aircraft condition/life informationand this enables the on-board system to carrythe history of the aircraft. Storing the aircrafthistory in a concise UI format providesoperational, management and safety benefits.For example, as improved damagecomputation methods or new knowledge fromfatigue tests emerge, retro computations can bereadily performed for each individual aircraftwithout the need for large amounts ofhistorical flight data. By simple data miningtechniques, prognostic relationships can bederived to link aircraft usage patterns toequipment failures and unscheduledmaintenance.

Data Integrity Corruption and CorrectionSmiths has developed a generic algorithm forautomatically detecting and correcting shortperiods of corruption, noise filtering and lostsignal reconstruction in monitored data. Thealgorithm has been developed using recordedflight data from a range of aircraft types andhas been supplied for automatic datacorrection on the F-35 JSF programme.

‘Virtual Sensors’ Using flight data information, the FUMS™

model-based network is able to generateparametric ‘virtual sensors’ that synthesiseinformation and avoid the installation andpenalties of ‘real’ permanent on-vehiclesensors that may otherwise impose weight,space and reliability issues. Once trained,FUMS™ can synthesise strains, structural loadsand stresses, fatigue, rotor torque, aircraft All-up-weight (AUW), Centre of Gravity (CG),aero-engine metal temperatures, thermaltransient stresses, Low Cycle Fatigue (LCF),High Cycle Fatigue (HCF), creep and crackgrowth. Virtual sensors offer an affordable,non-intrusive increase in aircraft sensingtechnologies without increasing aircraftweight. Smiths are currently developingvirtual sensors for the F-35 JSF where datafrom the JSF SPHM system will be used to

monitor the health of the critical componentsand perform Individual Aircraft Tracking(IAT) of the JSF fleet.

5.1.2 FUMSTM Applications

FUMSTM has been configured and applied to theanalysis of data from a wide range of militaryhelicopters such as Chinook, Lynx and Apachehelicopters, fixed-wing aircraft including JSF,Eurofighter, F16, Tornado and Harrier and aero-engines including the RB199 and Pegasus.Applications of FUMSTM capabilities and toolsinclude the following:

Selected as the JSF Force Life Management(FLM) system. The FLM system will performadvanced diagnostics and prognostics,individual aircraft and fleetwide life tracking,detailed engineering analysis and integratewith the JSF Autonomic Logistics InformationSystem (ALIS).

Analysis of flight data downloaded fromTornado, Typhoon, Apache, Chinook, Lynx,F16 and Hercules.

Support of life management programmes forthe Harrier engine fleet by analysing MoDlogistics data.

Analysis of UK MoD fast jet aero-enginevibration data acquired via test-beds byground-based Vibration MonitoringEquipment (VME).

Validation of structural, engine and avionicsusage and characteristics against design data.

Review and management of the usage of UKMoD Chinooks and Apache helicopters.

Management of MoD GenHUMS ConditionIndicators (CIs).

Validation of the serviceability of on-boardsensors and provision of ready access to flightinformation to assist first line maintenance.

Support of the qualification process for theMerlin HUMS on-board system.

Investigation of rare helicopter faults such asUncommanded Flight Control Movements(UFCMs).

Fusion of oil wear debris and flight data toproduce enhanced diagnostics, prognostics anddecision-making.



5.2 Intelligent Decision Support Tools

AI-based Intelligent decision support tools, suchas those within FUMSTM are being used toimprove the effectiveness of existing and newhealth monitoring systems. There is particularfocus on the requirement to improve the detectionof anomalous trends in health monitoring data toassist the transition to achieve CBM. This ismotivated by a number of factors associated withthe validation of monitoring data:

Existing potential for false alarms.

Expertise required to understand thesignificance of trends of multiple conditionindicators and determine the correspondingmaintenance action.

Difficulty in gaining experience to interpretmultiple trends associated with rare faults.

Under the Probabilistic Diagnostics & PrognosticSystem (ProDaPS), a DUS&T programme, jointlyfunded by the US Air Force Research Laboratory(AFRL) and Smiths Aerospace, Smiths hasdeveloped Artificial Intelligence (AI)-basedtechnologies in data mining, anomaly detection,information fusion, reasoning and decisionsupport for multiple on-board and ground-basedapplications [9]. These technologies have beenapplied to helicopter HUMS data, US Air Forceengine performance data and airline FlightOperation & Quality Assurance (FOQA) data.

The core tools developed under ProDaPS includea state-of-the-art data mining tool incorporating asuite of learning algorithms. Using the ProDaPSprobabilistic cluster algorithm, this has beenfurther developed to produce a unique anomalydetection capability. ProDaPS also includes aprobabilistic reasoning and decision support toolbased on Baysian networks. Probabilisticnetworks offer significant advantages for in-fieldapplications as they can model uncertainty, canaccommodate missing or conflicting data and canbe updated with in-service experience.

Following early work funded by the UK CAA[10], Smiths has worked with Bristow HelicoptersLimited (BHL) on a further CAA-sponsoredprogramme [11], to apply these AI-based anomaly

detection techniques to the analysis of HUMSVibration Health Monitoring (VHM) in both anoff-line and an in-service environment. Most AI-based approaches assume that the training data setused to train and construct the model comprisesexclusively normal, (i.e. healthy) data. However,HUMS VHM data presents additional challengesas ‘normal’ data will vary from one gearbox toanother and, due to limited feedback fromgearbox overhauls, it may not be known whetherall the components included in the training set aretruly healthy. The modelling approach musttherefore assume that any training data set willcontain some anomalous data and Smiths hasdeveloped enhancements to its anomaly detectionprocess, including trend extraction pre-processingalgorithms and a two-stage data modellingprocess, that overcomes such additionalchallenges. The anomaly detection system wassuccessfully demonstrated on BHL’s database ofhistorical AS332L HUMS data. It was shown thatthis process clearly detected the important testcase of a cracked main gearbox (MGB) bevelpinion that was missed by the currently installedHUMS. As part of the live trial, BHL used thisintelligent decision support system via the Smithsweb-based infrastructure (described later in thispaper) on a daily basis to monitor their North SeaAS332L fleet.

This capability enables the operator to obtain aglobal picture of gearbox component behaviourthat clearly identifies abnormal trends, degree ofabnormality and provides a means ofindependently checking gearbox state following arelated HUMS alert. Such enhancements to thedecision making process reduces the operator’sworkload in managing HUMS, particularly wherethe operator may have to simultaneously managea number of different HUMS. With broaderapplication, it is anticipated that such decisionsupport technologies will accelerate the civilCBM credit validation process but also ultimatelycontribute to the implementation of CBM across arange of both civil and military applications.

5.3 Integrated Vehicle Health ManagementResearch Programmes

Smiths has teamed with a number of aerospaceOEMs and other companies to research theintegration of established and new aircraftmonitoring functions to achieve extended and



improved maintenance operational and safetybenefits [12]. Existing aircraft systems generallyhave a set of standalone computers, a disjointedset of data sources and are therefore limited intheir capability to perform integrated datacollection and monitoring. The evolution ofIVHM systems offers a solution by integrating allthe condition monitoring, health assessment andprognostics into an on-board open modulararchitecture and then supporting the operator withintelligent decision support tools such as thosepreviously described.

A number of enabling technologies provide asound basis for IVHM. One of these is theevolution of modern IMA architectures thatprovide a common physical and logicalmaintenance infrastructure, such as the SmithsCore Computing Systems (CCS) and ModularProcessing Systems (MPS) - selected for theBoeing 787, C-130 AMP and Northrop GrummanX-47 UAV. Another enabling technology is thedevelopment and publication of open standardsfor IVHM systems. These include the OpenSystems Architecture for Condition BasedMaintenance (OSA-CBM) standard that defines acommon structure and optimised IVHM systemwhere all the data is available to the operator in asingle location and format. Further enablingtechnologies include the range of intelligentdecision support tools, such as those developed bySmiths Aerospace. Two examples of Smithspartnered IVHM research programmes thatexploit such technologies are summarised below.

5.3.1 AEPHM

The ‘Advanced Electrical power PrognosticsHealth Management’ (AEPHM) programme wasan AFRL sponsored DUS&T initiative withBoeing Phantom Works and Smiths Aerospace inrespective prime and subcontractor roles [13].

The programme was motivated by the growingtrend towards increased use of flight-criticalelectrical components such as actuators, pumps,valves and fans and the safety, maintenance andoperational benefits achievable by providing earlyand accurate fault and failure prediction. Theprogramme included seeded fault and acceleratedwear tests of electrical components and themonitoring of electrical loads via a Smiths Power

Distribution Unit (PDU) to characterise faultmodes and develop corresponding diagnostic andprognostic models for AI-based causal network‘reasoning’ techniques. Using these techniques, itwas demonstrated that the actuator, fuel pump andelectrical arc faults could be automaticallydetected, measured, trended and alerted from theelectrical load data alone within the Smiths PDU.Hence, using the existing electrical system data asa part of a ‘virtual sensor’ can dispense with theneed to add additional dedicated sensors.

The AEPHM programme also demonstrated thatcausal networks used to fuse data for AI-basedreasoning can be established and enhanced withthe use of design data prior to in-field practicalexperience. Failure Mode and Effect Analysis(FMEA) design data is traditionally used in thedesign of new systems to identify possiblecomponent failure modes, failure rates, effects onsub-components and criticality. As such, a FMEAeffectively captures diagnostic information and isreadily mapped onto a causal network to constructa diagnostic reasoning engine. As in-fieldexperience is acquired, the causal network can beupdated to include updated probabilitydistributions. This enhances the data fusionprocess and hence the diagnostic and prognosticcapability.

5.3.2 TATEM

‘Techniques and Technologies for NewMaintenance Concepts’ (TATEM) is a four-year,€40 million project co-funded by the EuropeanCommission under its ‘Framework 6’ researchprogramme. The aim of the project is to showhow new integrated health managementtechnologies and techniques can improve aircraftavailability and reliability and reducemaintenance related costs for commercial airlinesby 20% within 10 years and 50% within 20 years.The project, led by Smiths, involves 56 partnersincluding airframers, airlines, suppliers andresearch organisations from Europe, Israel andAustralia.

The project required understanding currentmaintenance management processes includinglimited levels of health monitoring, the impact ofunscheduled maintenance, human factors and howintegrated on-board/off-board health monitoring,



diagnostics and prognostics could provide a rangeof benefits including CBM.

Smiths Aerospace health management researchfocused on how AI-based data mining andreasoning could support improved diagnostics andprognostics techniques for complex criticalcomponents such as electrical power andactuation systems by fusing performance andhealth parameters. The results, which have madeuse of real data scenarios, suggest the potential toprovide enhanced maintenance benefits in termsof reduced unscheduled maintenance anddowntime, improved maintenance scheduling,optimised maintenance intervals, reducedmaintenance activity and spares holding and anincreased information-to-data ratio.

IVHM: Changing Maintenance

At Gate Next Flight

Delay

Maintenance Opportunity

Hangar

Today’s Aircraft

Next Generation

Future IVHM

Figure 4: Example of TATEM Objective

For example, Figure 4 shows how, for a nextgeneration commercial airline, early detection,diagnosis and warning of degrading componentsallows the ground maintenance crew to prepareand reduce the time required for a turn-around. If,with an IVHM approach, a fault can be detectedearlier still and more accurately, the need toperform maintenance during turn-around might beeliminated and scheduled for conventionalhangar-based maintenance periods.

5.4 Integrated Ground System Support

Smiths has developed a range of genericcapabilities that support current HUMS operatorstoward an integrated IVHM capability. Theseinclude networked and remote ground systemproducts and services that can be applied to any

aircraft or vehicle operation. Two examples areillustrated below:

5.4.1 Web-based Support Services

Smiths has developed a comprehensive web-based support capability to assist its HUMSoperators in managing and achieving maximumbenefit by fully exploiting the informationavailable from HUMS data. The service involvesSmiths managing the exchange and flow of dataand information between the operator, Smiths andthe manufacturer. As far as possible, the transferof data and information is an automated process.Figure 5 illustrates the flow of HUMS data andinformation.

Data

Information

Operator:

Operations

Maintenance

Logistics

Manufacturer:

Engineering support

Logistics

Support

Design Office

Smiths:

In-field Support

Smiths:

Data Warehouse

Data Exploitation & Decision Support

Operator:

Flight Line

Aircraft:

Data Transfer

Data

Information

Operator:

Operations

Maintenance

Logistics

Manufacturer:

Engineering support

Logistics

Support

Design Office

Smiths:

In-field Support

Smiths:

Data Warehouse

Data Exploitation & Decision Support

Operator:

Flight Line

Aircraft:

Data Transfer

Figure 5: Web-Based Services

Operators’ HUMS data is automaticallytransmitted, via the internet, to the data warehouseon Smiths file servers where the data is processedvia Smiths proprietary data-exploitation decisionsupport toolset that convert processed HUMS datainto advanced information. In turn, the operator’soperations, maintenance, logistics and flight linedepartments (and the manufacturer as arranged)together with Smiths in-house and infield supportengineers are able to access this advancedinformation. However operators require only alocal ‘browser’ capability rather than full HUMSground station capabilities. Smiths manages theHUMS data and its effective exploitation via thestages of download, storage, distribution,interpretation and advice. In accordance with itsin-house capability, the operator is able to selectthe level of service and analysis/interpretationrequired. Where operators choose to manage theirdata, Smiths provides assistance with diagnosticand prognostic interpretation as required.



An example of a related support service is theprovision of the Smiths fault database. Smithsarguably holds the largest HUMS fault databaseworldwide, containing a large number of faulttrends and condition indicator exceedances. Aspart of its support service to operators, and withsupported operator’s permission, all fault data is‘sanitised’ to remove all operators’ identities andaircraft tail numbers and made freely available toall Smiths HUMS operators. As a consequence, aSmiths HUMS operator is able to obtain powerfulassistance when interpreting apparent fault databy accessing the Smiths Support Website to viewand compare his data with the database of realfault trends from corresponding aircraft andcomponents from many operations around theworld. The website incorporates a sophisticatedsearch facility so that the user can examine moregeneral or very specific data as required. Web-based services that fully exploit information tomaximise operational and maintenance benefitsprovide a firm basis for CBM maintenanceapproaches and for enhanced PBL support.

5.4.2 Intelligent Data Card

The Smiths Intelligent Data Card (IDC) has beendeveloped to support deployed aircraft andvehicles that do not have access to a dedicatedsupport infrastructure or HUMS ground stationand its corresponding data analysis capability.The IDC is a laptop compatible PCMCIA cardthat hosts the conventional HUMS advanceddiagnostics and prognostics analysis tools. TheIDC can be simply plugged in to a laptop thatitself requires a browser and a PCMCIA slot.When inserted, the IDC enables the review ofcritical HUMS data, recommended adjustments(such as helicopter RTB) and HUMS systembuilt-in test status.

5.5 FRES Technology DemonstratorProgramme (TDP)

This year sees the conclusion of the Team ISIS(Integrated Scalable Independent Solution) FRESEA TDP. As part of Team ISIS, Smiths wasresponsible for producing trade studies on thebenefits, technical effectiveness, diagnostics andprognostics of an E2E HUMS. Smiths was alsoresponsible for demonstrations of specific

elements of an example E2E land-vehicle HUMS.Whilst it is anticipated that production FRESHUMS will be part of an integrated modularvetronics architecture, for the purpose of the TDPdemonstration, the on-board system was based onexisting commercial-off-the-shelf (COTS)hardware and software. The demonstrationsincluded a mobile trial on a General Dynamics’Advanced Hybrid Electric Drive (AHED) combatvehicle and a Systems Integration Laboratory(SIL) demonstration.

Figure 6: FRES TDP HUMS Demonstration

During the mobile trial, Smiths demonstrated thesuccessful acquisition of on-vehicle HUMS datafrom both dedicated sensors and the vehicle EAdatabus followed by wireless transfer of aSituation Report (SITREP) from the vehicle to theoff-vehicle HUMS Base Station some distanceaway via high-bandwidth WiFi communicationsand UK Army Bowman digital radio. The use of aruggedised laptop to collect and display HUMSdata remotely from the vehicle was alsodemonstrated.

As part of the SIL demonstration, Smithsdemonstrated the response of the HUMS system



to a genuine fault (an engine inlet filter breachcaused by high levels of sand ingestion detectedby new technology electrostatic sensors).Following accurate fault diagnosis, the diagnosticinformation was transferred through theinformation management chain from the vehicleto the Information Management System (IMS)and the Joint Asset Management SystemEngineering Solution (JAMES). Thedemonstration also included the generation of aCasualty Report (CASREP) and automatedvehicle availability status, showing how HUMSdata may be used to provide operational decisionsupport.

As part of the FRES TDP, Smiths demonstratedtwo leading-edge capabilities that representrespective ends of the FRES E2E HUMS. At oneend was an electrostatic-based filter integritysensor. This was derived from a similar SmithsCOTS electrostatic sensor previously used tomonitor UK Warrior vehicles and which exploitsthe same technology used to perform aero-enginegas path debris monitoring on the JSF aircraft.The electrostatic sensor successfully detected theingestion of the sand particles and alerted thefilter’s compromised integrity. At the other end ofthe E2E HUMS and in collaboration withLockheed Martin, Smiths demonstrated theintegration of HUMS with the operationallyfielded JAMES logistics system with successfultwo-way communication. It was clearly shownthat health status and ultimately fault informationgenerated by the HUMS on-board system wasreadily available to the JAMES and that theJAMES was able to communicate an alarm-acknowledgement back to the on-board HUMS.

Both mobile and SIL demonstrations illustratedthe capability of integrated HUMS and IVHMtechnologies, in conjunction with operationallyfielded communications and IMS, to reducewhole life costs through improved maintenancebut also the huge operational and forcemultiplying benefits available to a fleet of fightingvehicles. These include improved collectivesituational awareness, through the accurate andtimely health and consumable status reporting;improved mission effectiveness, planning andprobability of mission success through accuratefault and failure prognosis.

6. CONCLUSIONS

The safety, maintenance and operational benefitspromised by comprehensive helicopter HUMS aregenerally being realised. This HUMS technologyis mature and is the basis for continued growthbased on justifiable through life cost benefits. Theapplication of comprehensive HUMS and fleetmanagement technologies to fixed-wing aircraftand land vehicle is evolving. Indeed, the advent ofintegrated modular electronics architectures andnetworked logistics systems supports theintegration of health monitoring and managementwith on-board and ground based systems and theconcept of Integrated Vehicle HealthManagement (IVHM).

The prospect of transitioning traditionalmaintenance to include a condition-basedmaintenance approach is attracting considerableinterest and would appear to have positiveimplication for modern performance basedlogistics contracts. Whilst the existing, matureHUMS diagnostic capabilities provide the originalbasis for including a CBM approach, evidencesuggests the combination of these with evolvingintelligent decision support technologies, such asthose being developed by Smiths Aerospace willaccelerate the implementation of CBM across arange of both civil and military applications.

Suppliers of HUMS and IVHM technologies arenow challenged, more than ever, to providegeneric, flexible, expandable and cost-effectivecapabilities that can simultaneously providethrough life benefits to a wider range ofapplications and users with differentenvironments, operating and maintenance culturesand operator skill levels. With a principal focuson enhancing the quality of information toachieve a “right information, right place, righttime” approach, Smiths has developed andcontinues to develop flexible and affordablesystems, tools and techniques to supportprogrammes and initiatives that bring increasingbenefits to a wider range of applications andusers.



7. REFERENCES

[1] Helicopter Safety Study 2, SINTEF, 15December 1999.

[2] “The Operational Benefits of Health & UsageMonitoring Systems” A Draper & J Gourley,DSTO HUMS 2003, 17-18 February 2003.

[3] “Evaluating the Benefit of Health & UsageMonitoring Systems” A Heather, AHS 59th

Annual Forum 6-8 May 2003.

[4] MoD Defence Standard 25-24, Health &Usage Monitoring Capability for Land Platforms(HUMS), Issue 1, 24 December 2004

[5] AC 29-2C MG 15 “Airworthiness Approval ofRotorcraft Health & Usage Monitoring System(HUMS)”.

[6] “HUMS Condition Based Maintenance CreditValidation” B D Larder, M W Davis. Approvedfor presentation at AHS 63rd Annual Forum 1-3May 2007 [7] “FUMSTM Fusion and Decision Support ForIntelligent Management of Aircraft Data”, H AAzzam, P Knight, J Cook, E Moses, IEEEAerospace Conference, March 2006.

[8] “The UK MOD EUCAMS Strategy and TheFUMSTM Developments”, S Driver, M Robinson,E Moses, H Azzam, J Cook and P Knight, 2007IEEE Aerospace Conference, Mar 2007.

[9] “The Implementation of Advanced Diagnosticand Prognostic Concepts – Practical Tools forEffective Diagnostic and Prognostics HealthManagement”. R E Callan and B D Larder, IEEEAerospace Conference, Montana 2003.

[10] “A Demonstration of the Feasibility andPerformance of an Intelligent ManagementSystem Operating on HUMS In-Service Data,” HAzzam and N Harrison, CAA Paper 99006, 1999Civil Aviation Authority, London, September1999.

[11] “Intelligent Management of HUMS Data:Development and Demonstration of a VibrationHealth Monitoring Anomaly Detection System” B

D Larder, R E Callan, S Salter, AmericanHelicopter Society 62nd Annual Forum 9-11 May2006.

[12] “The Use of On-board Condition Monitoring,Usage Monitoring, Diagnostics, Prognostics andIntegrated Vehicle Health Management toImprove Aircraft Availability and MissionReliability”, J Dunsdon, NATO RTO AVT-144Workshop, Vilnius, 3-5 October, 2006.

[13] “Aircraft Electrical Power Systems Prognostics and Health Management”, K. Keller, et al, IEEE Aerospace Conference, Montana, 2006.


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