1

5

6

Contents

Part I Keynote Lectures

ME2016_KL01: A Maintenance Programme: Recollections and Reflec-

tions………………………………………………………………..…………….12

John Harris

ME2016_KL02: What Does the Future of Maintenance Engineering Look

Like?................................................................................................... ...................21

Gary Knight

Part II Contributed Papers (also included in Journal

of Maintenance Engineering, Volume 1)

ME2016_1102: Major Accident Hazard (MAH) Prevention – just TIPS of

D’AIS BERG ........................................................................................................33 Mohammad Fuad Bin Abdullah

ME2016_1104: Investigation of High Piping Vibration ...................................39 Keri Elbhbah, Jyoti K. Sinha, Wolfgang Hahn

ME2016_1105: Identifying Organisational Requirements for the Implementa-

tion of an Advanced Maintenance Strategy in Small to Medium Enterprises

(SME) …………………………………………………………………………....48 David Baglee, Erkki Jantunen, Pankaj Sharma

ME2016_1106: Development of a Quantitative Maintenance Model..............58 María del Carmen García Lizarraga, Jyoti K. Sinha

ME2016_1107: A study of unsteady force on the stem in a valves with differ-

ent openings .........................................................................................................69 Yu Duan; Alistair Revell; Jyoti K. Sinha; Wolfgang Hahn

ME2016_1108: In-situ Vibration Behavior of an Offshore Wind Turbine….80 Jyoti K. Sinha, Erfan Asnaashari, Ian Andrew, Andy Morris and Wolfgang Hahn

ME2016_1109: Precaution during the Field Balancing of Rotating Ma-

chines…………………………………………………………………………….90 Sami M. Ibn Shamsah, Jyoti K. Sinha, Parthasarathi Mandal

7

ME2016_1110: An Identification of the Enabling Factors for the Develop-

ment of a Unified Approach to Maintenance Strategy Development within the

Automotive Supply Chain……............................................................................98 Derek Dixon, David Baglee, Kenneth Robson, Antti Ylä-Kujala and Pankaj Sharma

ME2016_1111: Sensitivity Analysis of Peak Decay for Acoustic Input Pulse in

Pipeline Inspection..............................................................................................112 Mohd S A M Yusoff, Jyoti K. Sinha, P Mandal

ME2016_1112: eROMEO Model: A Proposed Maintenance Strategy to En-

hance Plant Reliability.......................................................................................122 Gabriela Fernanda Sánchez Espinoza, Pedro Osvel Nuñez Izaguirre, Jyoti K. Sinha

ME2016_1113: Fault Identifications of Industrial Planetary Gearbox under

Varying Load Operation using Empirical Modeling and Vibration Analysis

Techniques……………………………………………………………………..132 Somporn Duangbuntao and Tutpol Ardsomang

ME2016_1114: A Practical Guide to Implementing a Successful Vibration

Programme………………………………………………………………….....144 Michalis Hadjiandreou

ME2016_1115: Practical Application of Integrated Failure and Decision

Analysis for Maintenance and Reliability Improvement...............................155 Frederick Appoh, Akilu Yunusa-Kaltungo

ME2016_1116: A simplified rotor-related faults detection approach based on

a combination of time and frequency domain features.................................167 Kenisuomo C. Luwei, Jyoti K. Sinha and Akilu Yunusa-Kaltungo

ME2016_1117: Detection of Bearing Fault within Helicopter Gearbox with a

Novel Condition Indicator.................................................................................178 Faris Elasha, David Mba

ME2016_1118: Overlooked Opportunity - Steam Traps Study....................190 Mohammed S. Khulaify

ME2016_1120: Creating a “Plan for Zero” Downtime...................................199 Mick P Saltzer

ME2016_1124: Non-contacting Mechanical Seals Selection Considerations for

Steam Turbines: A Case Study of 102-GT-314 Saudi ARAMCO, ABQAIQ

Plant………………………………………………………………………….…213 Eng. Andres Gonzalez

8

ME2016_1125: Strain Based Monitoring for Flutter Evaluation and Assess-

ment of Mitigation Approaches in Transonic Compressor of a Turbofan En-

gine ................................................................................................................ ......224 Owais Majid Kamili, Reshma Khan, K. Vishwanath, S. Ganga Bhavani,

T.N.Suresh

ME2016_1130: CGTGS Frame-6B Oil Mist Eliminator Reliability Enhance-

ment ................................................................................................................ ....238 Abdullah S. Al-Barqi

ME2016_1133: Learning from Alaska Airlines Flight 261’s Accident..........246 M. Almatani, A. Al-Moqbel, W. Alqurashi

ME2016_1135: Case Study: The Detection and Diagnosis of Screw Compres-

sor Bearing Defects using Vibration-based Condition Monitoring...............259

Jamie Borley

ME2016_1136: The Design and Proposal of a Condition Monitoring and

Analysis System for Rotating Equipment at a Brewery…............................270 Jamie Borley

ME2016_1139: Experimental Studies on Acoustic Leak Detection in Steam

Generators of Fast Breeder Reactor...............................................................283 Ranga Ramakrishna, S.Kishore, S. Chandramouli, V.A. Sureshkumar, I.B.

Noushad, V. Prakash and Dr. K. K. Rajan

ME2016_1140: Obsolescence Management- A Practical Perspective.........296 Javed Habib

ME2016_1142: High Speed Balancing of Rotors – in which Mode?............306 Ronald Janzee, Atul Nath

ME2016_1143: A Holistic Approach for Anticipative Maintenance Planning

Supported by a Dynamic Calculation of Wear Reserve...............................313 Robert Glawar, Christoph Habersohn, Tanja Nemeth, Kurt Matyas, Burkhard

Kittl, Wilfried Sihn

ME2016_1146: Traditional In-Situ Gas Compressor Rotor Balancing: A Case

Study……………………………………………………………………..........325 Mamdouh M. IbnShamsah, Sami M. Ibn Shamsah

ME2016_1148: Gaining Lessons from Previous Failures: A Case Study on

Imperial Sugar Company (ISC) Explosion.....................................................333 Waleed Al-Qurashi, Akilu Yunusa-Kaltungo, Mohammed Almatani , Abdulaziz

Al-Moqbe

9

ME2016_1149: Investigation of Cost Effective Maintenance Framework for

Centrifugal Compressors...................................................................................345 Xiaoxia Liang, Fang Duan, Ian Bennett, David Mba.

ME2016_1150: Multidimensional Prognostics for Rotating Machinery – A

Review ........................................................................................................ .......357 Xiaochuan Li, Fang Duan, David Mba, Ian Bennett

ME2016_1151: Helicopter Gearbox Bearing Fault Detection using Kurto-

gram and Envelope Analysis............................................................................ .374 Linghao Zhou, Fang Duan, Matthew Greaves, Faris Elasha, Suresh Sampath, Da-

vid Mba

ME2016_1154: Frictional Effects on the Diagnostics of Helical Gear Tooth

Defects ............................................................................................................ ...383 Khaldoon F. Brethee, Xiaojun Zhou, Fengshou Gu, Andrew D. Ball

ME2016_1155: Application of Mixture Models to Survival Data................394 Sílvia Madeira, Paulo Infante, Filipe Didelet

ME2016_1159: Prognostic Studies in Fatigue Cracked Rotor System........403 Rahul Pardeshi, A. K. Darpe, K. Gupta

ME2016_1160: A study of two bispectral features from envelope signals for

bearing fault diagnosis…...................................................................................412 Ibrahim Rehab, Xiange Tian, Niaoqing Hu, Tianxiao Yan, Ruiliang Zhang, Feng-

shou Gu and Andrew D. Ball

ME2016_1165: A Framework for Business Model Development for Reaching

Service Management 4.0...................................................................................426

Mirka Kans, Anders Ingwald

ME2016_1166: Ultrasound - Optimizing your Lubrication Process............437 Gary Rees

ME2016_1162: New Level of Performance with Dynamic Maintenance Man-

agement: Achieving Excellence in Four Dimensions…...................................441 Thomas Frost, Jamie McCarthy

ME2016_1163: Analyzing Industrial Failures with Reliability Tools: A Case

Study of a Pipeline Failure................................................................................453 Abdulaziz Al-Moqbel, Akilu Yunusa-Kaltungo, Mohammed Almatani Waleed Al-

Qurashi

10

ME2016_1164: Investigation the need of Total Productive Maintenance

(TPM) Framework Development for Deployment within the Iraqi Power

Generation ........................................................................................................465 Darkam Aljanabi, Martin Birkett, Chris Conner

ME2016_1167: Design of an Online Vibration Monitoring System for a Hy-

draulic Turbine-Generator (TG) Set..............................................................475 Gabriel Orquera, Akilu Yunusa-Kaltungo, Jyoti K. Sinha

ME2016_1168: Review of Pipelines Integrity Management Processes........490 Edet Afangide, K. B. Katnam, Jyoti K. Sinha

ME2016_1169: A Simple Practical Approach for Design of Vibration-based

Condition Monitoring System through a Case Study.....................................503 Buyung Baskoro, Akilu Yunusa-Kaltungo, Jyoti K. Sinha, Abdulaziz Al-Moqbel

ME2016_1170: Reliability Investigation Critique of a Wind Turbine..........511 Richardson Cleetus, Gabriel Orquera

ME2016_1171: Developing Maintenance Strategy for Wind Turbine on Off-

shore Platform………………………………………………………………….523 Yang Cao, Gabriel Orquera

ME2016_1172: Potential Reasons of Turnaround Management Failures....538

Xijiang He

Part III Contributed Papers

ME2016_1103: Technical Economic Evaluation of the Configuration Options

for the Loading Crude System of the Distiller Unit N (DU-N)……………..546

Luis Daniel Khalil

ME2016_1122: Lessons Learned from a Survey of 300 Maintenance Techni-

cians on How to get Satisfied Maintenance Technicians and Increase Reliabil-

ity Culture……………………………………………………………………..563

Wim Vancauwenberghe

ME2016_1123: A Journey Towards Enhancing the Safety and Reliability of

Power Boilers through Steam Boilers Re-tubing Projects…………………572

Mohammed Omar Alghamdi

ME2016_1126: Dry Gas Seal Best Operating Practices and Reliability En-

hancement in Centrifugal Compressor………………………..…………….573

Bibekananda Bisoyi

11

ME2016_1134: Material Forecasting and Procurement Continues Improve-

ment Initiatives in an Oil and Gas Facility…………..………………………581

Nasser Al-Shahrani, Mefleh Al-Hajri

ME2016_1137: Simulating the Impact of Repair Strategies on Repairable

Spare Part Provisioning in a Multi-echelon Replenishment System……….583

Peter Chemweno, Peter Muchiri, Liliane Pintelou

ME2016_1138: Detection of Rotating Machine Faults using Vibro-acoustic

Techniques……………………………………………………………………..584

Mohammed IMT Khandakji, Akilu Yunusa-Kaltungo

ME2016_1141: Enhanced Turnaround Model for Oil & Gas Facility…….585

Nasser Al-Shahrani, Jyoti K. Sinha, Hussain M. Al-Qahtani

ME2016_1147: A Model-based Prognostic Approach to Predict Remaining

Useful Life of Components……………………………..…………………….587

Madhav Mishra, Matti Rantatalo, Johan Odelius, Uday Kumar

ME2016_1156: Condition Monitoring for Predictive Maintenance – Towards

Systems Prognosis within the Industrial Internet of Things…………..…..589

Douglas O. Chukwuekwe, Tommy Glesnes, Per Schjølberg

ME2016_1173: The Impact of Implementing Reliability Analysis on

Maintenance Strategy Optimisation: Case Study of Abu Dhabi Power

Utilities………………………………………………………………………...600

Abdulla Y. Alseiri, Ayedh M. Albreiki, P. Farrell

313

A Holistic Approach for Anticipative

Maintenance Planning Supported by a Dynamic

Calculation of Wear Reserve

Robert Glawar1, Christoph Habersohn

2, Tanja Nemeth

1, Kurt Matyas

1,

Burkhard Kittl2, Wilfried Sihn

1,3

1 Institute of Management Science, Vienna University of Technology, Theresianumgasse 27,

1040 Vienna, Austria

2 Institute for Production Engineering and Laser Technology, Vienna University of

Technology, Getreidemarkt 9/311, 1060 Vienna, Austria

3 Fraunhofer Austria Research GmbH, Division of Production and Logistics Management,

Theresianumgasse 7, 1040 Vienna, Austria

Email: robert.glawar@tuwien.ac.at; habersohn@ift.at;

tanja.nemeth@tuwien.ac.at; kurt.matyas@tuwien.ac.at; kittl@ift.at;

wilfried.sihn@tuwien.ac.at; wilfried.sihn@fraunhofer.at

Abstract Production lines are exposed to performance-related wear effects. By

carrying out maintenance measures at the right time, plant availability, product

quality and process efficiency can be secured in modern manufacturing systems.

While conventional maintenance strategies do not combine those strongly related

aspects, holistic and anticipative quality- and maintenance strategies take different

maintenance and load related data into consideration. Therefore, effects such as

unnecessarily high maintenance efforts, wasted resources and the occurrence of

quality and availability losses may be reduced and avoided. In this paper a holistic

approach for maintenance planning, that will be able to forecast and anticipate

failure moments by steadily compiling and correlating various data like condition

monitoring data, wear data, quality and production data via “cause and effect” co-

herences is presented. By breaking down the production facilities on component

level and allocating defined load-data to specific components a basis is set to link

load data and historical data by a dynamic calculation of wear reserve. The con-

sistent compilation of data for each complex of loads acting on a component as

well as its correlation of failures, enables the planning of rule-based anticipative

maintenance measures. A response model based on this logic, performs a multi-

variate optimization of maintenance and repair measures. This approach allows to

anticipatively identify maintenance critical conditions as well as the prediction of

failure moments and quality deviations.

Key words: Maintenance Planning, Predictive Model, Calculation of Wear Re-

serve

Proceedings of 1

st International Conference on

Maintenance Engineering, IncoME-I 2016

The University of Manchester, UK

Paper No ME2016_1143

314

1.0 Introduction

As a result of rising cost pressure in combination with the increasing of

complexity in production, handling and transport facilities as well as the need for

individual products based on customer requirements, maintenance is becoming a

key role in manufacturing systems. The preserving of the function and

performance of a machine or system, which constitutes the main task of

maintenance, is gaining more importance, since the demand for higher quality and

more efficient production processes in highly volatile environments, is

continuously increasing [1, 2]. To achieve the desired production targets, quality

management, maintenance and production control depict essential functions in

manufacturing systems [3].

Based on different reasons, the use of classical maintenance strategies, for

deriving a minimum between maintenance costs and system-wide downtime, is

getting more and more difficult.

Due to incomplete or delayed provision of information regarding the machine

status, it is impossible to ensure a contemporary, exact and wear optimized re-

placements of components.

In case quality of maintenance relevant data is often unsatisfactory, system-

wide standards are missing or the same data sets are processed in different sys-

tems within the period between failures, it is not possible to derive a significant

correlation and analysis of the failure behavior.

Since the theoretical loads vary from the applied load profiles, the explanatory

power of static lifetime calculations for plant components decreases.

Hence the maintenance measures are conducted at a wrong or unfavorable time.

Thus, it is not likely to achieve a replacement of components that is aligned with

the present production status and the required product quality. The approach of

classical maintenance strategies is to buy improved plant availability with

increasing maintenance costs that ensue from wasting resources. For that reason,

existing planning tools are facing little acceptance.

However, in the light of “Industry 4.0” it is foreseeable that more and more data

regarding the machine status will be available in real time and may therefore be

used for maintenance planning [4]. Furthermore, data mining methods are

successfully applied in the field of maintenance, in order to identify failure

patterns out of historical data or condition based monitoring [5]. Therefore, it will

be possible to accomplish and maintain a knowledge management for the

maintenance relevant data [6, 7]. In this context the foundation is set in order to

derive dynamic load profiles depending on the scheduled production program and

consequently use this information for anticipative maintenance planning.

315

2.0 Challenges to Anticipative Maintenance Planning

In order to achieve an improvement in the systems productivity, decisions have to

be made in the area of conflict between economy, safety and availability in order

to simultaneously achieve a minimization of costs, a maximization of equipment

availability and increased product quality by the applied measures.

2.1 Maintenance Strategies

Conventional maintenance strategies can still be applied in context of large-scale

production where constant machine loads are applied. However, this is not the

case for customer order driven production with specific load spectrums. In order

that the increasing demand for higher quality and more efficient production

processes is ensured, new holistic concepts in addition to the three basic

maintenance strategies, failure oriented maintenance, periodic maintenance and

condition-based maintenance have to be developed and established (Figure 1).

Figure 1 Maintenance Strategies [1]

The need for anticipative holistic maintenance strategies, considering sensor

signals from condition monitoring systems, quality and machine data, historical

information regarding the failure behavior as well as the medium-term planning of

production, arises especially in flexible production systems with a high variation

of the production mix and no fixed load spectrums [8-9].

2.2 Maintenance Approaches

One of the central issues within maintenance planning approaches is to predict the

degradation and its effects on the manufacturing processes. Already existing

methods cover different aspects of quality control (product, process and

production infrastructure) (Figure 2).

316

Most existing models, used to forecast machine failures, are based on historical

data or information from long term studies about the condition of the machine or

its components. These data sets are a prerequisite for probability models, like the

Weibull distribution, that describes typical degradation processes of components.

[9]. Traditional quality management methods use product- and process data for

gaining conclusions concerning product quality, which subsequently (after a data

analysis) trigger a re-adjustment of the production process, if a deviation from the

defined specifications is detected. However, neither machine data, nor production

planning and control data are included in this approach. Maintenance strategies

that focus mainly on quality and combine product and machine level by linking

the product quality with failure effects of certain components, are provided in the

literature. In further consequence, a coherence between quality relevant

characteristics and degrading of the machine can be deduced [10]. Load oriented

maintenance strategies statistically determine the remaining life time by using

external measurement parameters. In order to schedule maintenance intervals,

machine- and process perspective are combined by linking the production program

and failure effects of components [11]. Using a load spectrum dependent service

life model allows for aligning operating resources in terms of economical

organizational and technological aspects [12].

Figure 2 Interactions of maintenance related perspectives

However, a holistic maintenance strategy that considers product, process and

machine perspective at the same time, is, even though certain maintenance

policies combine different perspectives, neither applied in the industrial practice

nor published in literature. Currently present solutions for maintenance planning

do not combine condition monitoring data, quality management data, recorded

machine loads as well as acquainted downtime patterns. The correlation of these

data sets in combination with suitable system models may be used as a basis for

decisions regarding optimized maintenance measures, product quality and energy

consumption.

Internal

M achine(components,

failure effects)

External

Maintenance planning

Input

OutputProduct

(quality)

Process(product ion

programme)

Quality oriented

maintenance

Load oriented

maintenance

Tradit ional

maintenance

317

3.0 Methodology to Combine Product, Process and Machine

Perspective

The presented approach is based on the systematic description of product quality-,

process- and machine perspective as well as the analysis of already existing

fractional solutions for this perspectives. In further consequence a reaction model

is able to forecast and anticipate failure moments. This model offers a set of rules

which are proposing maintenance measures anticipatively. Those rules are

supported by condition based fault diagnostics such as simulation processes.

Moreover, data based methods such as data mining will be used to support the

validity of the reaction model.

This model is realized within the following 4 steps (Table1):

Table 1 Model-realisation within a 4 steps-process

Step 1: Development of a framework

Illustration of production facilities on component level incl. their load profiles

Identification of the current condition states of real process objects and

information-transfer to the developed framework

Step 2: Data analysis and simulation study

Selection and preparation of historical data (product quality, process

and machine data), condition monitoring data and load data

Exploration of maintenance relevant parameters to derive conclusions concerning load-induced wear and quality deviations

Step 3: Parameter study

Correlation and compression of the derived parameters

Validation and classification of the determined interrelations of parame-ters

Step 4: Development of the anticipatory maintenance model

Derivation of generally applicable rules

Implementation and test run of the model

Step 1: In order to capture the behaviour of the system, the machine is split up in

its maintenance relevant components. Due to machine-data, such as positioning of

the tool slide, current consumption of drive units, data of accelerator and

temperature sensors etc., load profiles can be determined analytically. If additional

machine or tool-data is necessary, appropriate sensors and measurement devices

have to be installed.

Step 2: Parallel to the development of a generally applicable system-framework,

maintenance relevant historical data from various data collecting systems is

analyzed to enable the identification of failure effects and the detection of quality-

318

relevant cause-and-effect coherences. By a logical linkage of these datasets

maintenance relevant machine parameters are derived.

In order to reveal necessary load profiles (such as rotation speed, speed,

acceleration profiles) as well as cutting volumes of each NC-program, a

simulation study is conducted. Together with the structure of maintenance relevant

components, the simulator is able to estimate the prevalent process forces. As a

result, load/time-functions for each NC-program can be determined.

Step 3: Load spectra of identical machining centers, but with different production

program, are compared and correlated with previous identified failure effects, to

gain further information about maintenance relevant influencing factors. A

validation and classification of the explored parameters is therefore possible.

Step 4: As a last step, generally applicable planning rules for the proposed model

are derived. Data-mining methods within this process-step enable a half automatic

and rule-based data correlation and creation of prognoses concerning the condition

of machine and tool components and variations in product quality.

The generated knowledge about maintenance and quality relevant trends

throughout the manufacturing process is linked with current production planning

data. As a result, the developed model is able to suggest anticipatory quality- and

maintenance measures by the integrated set of rules. These rules can be used in a

maintenance planning tool where they form the basis for decision making. With a

suitable set of rules, it is possible to forecast failure, to visualize wear and to

predict quality trends for single machine components as well as a whole system.

As a consequence, the utilization of necessary resources can be optimized to

reduce maintenance cost and the plant availability can be increased, since

maintenance measures are initiated in time and in coordination with the whole

production line and the production program.

An approach model for the realization of load oriented maintenance planning rules

is described later in this paper (Section 5)

4.0 Dynamic Calculation of Wear Reserve

A requirement for an anticipative maintenance strategy is reliable data, based on

which the foresighted calculation of the time of failure of a machine or a compo-

nent is enabled. For the specific machine components, a stock of wear-out is de-

fined in line with DIN 31051, which is continuously exhausted by the production

processes. By taking into account the scheduled production, reliable forecast of fu-

ture wear-out can be calculated and a timely set maintenance order can be placed

when a certain wear-out limit is reached (Figure 3).

The basis for this calculation is the incremental wear of the component caused by

every single produced part. In large scale production these values can be obtained

319

by an empirical analysis as well as statistical methods (mentioned in Section 3), as

a correlation between a sufficiently big number of similarly produced workpieces

and specific maintenance steps can be distinguished.

For small batch, individual manufacture and new products, the only viable solu-

tion to obtain the incremental wear out of a machine component is an analytic ap-

proach or the use of simulation methods.

Figure 3 Wear-out of a component

Collected machine data is divided into condition- and load data. For the collection

of condition data cyclic and continuous processes are applied in condition moni-

toring systems. Cyclic procedures are used to detect damage curves. The status pa-

rameters are hereby collected based on test programs in de-fined time intervals of

a few days or weeks. Based on the test results, predictions of the damage evolution

are created, using trend analysis [13]. The processing of the sensor signals into a

load profile can be divided into two steps. The raw signal is first compressed at the

control level and then forwarded to the parts- or component level to create the load

spectrum (Figure 4). The sum of the load spectra on the parts level results in the

load spectrum of a single component. The knowledge about the load spectrum en-

ables the determination of the lifespan of the component and thus also of the

availability of the machine.

Figure 4 Processing of sensor signals into a load profile

320

5 0 Approach Model for Load Oriented Maintenance Planning

Rules

The starting point of such an approach is defined by the CAD data of the work-

piece combined with the NC-code for the machine tool, which enable the determi-

nation of real axis motions and accelerations revealing the inertia forces, whereas

the material removal rate leads to the calculation of the cutting forces. Further the

individual switch commands such as tool clamping and coolant activation can be

extracted from the NC-code.

Figure 5 Processing structure

The calculation of the wear of a single processing involves three successive steps

(Figure 5). First the feed path and the ablation volume is calculated by the soft-

ware CHECHitB4, developed by the company Pimpel. An archive file containing

all relevant technological data of a machine tool with a Siemens control can be

preloaded in the software to define the production environment and the NC-

programs. The simulation is based on a NC core by Siemens and hence delivers

the exact feed path of the process. CHECHitB4 then delivers the axis motion data,

the chip volume as well as the switch commands needed for the dynamic compu-

tation. In a second step the drive and guidance loads are calculated using an ana-

lytical multi-body model during dynamic computation, which then are passed on

to wearout calculation. The wearout computation distinguishes three cases (Fig-

ure 6):

1) Switch-dependant components: The lifetime of such components de-

pends on the number of switch-commands they undergo as for example

when one is considering the life-time of a valve. In this case the total

number of switches is counted and normalised to 1.

321

2) Time-dependant components: These components undergo wear only

when subjected to a duty cycle. The wearout calculation takes into account

the total duty cycle time and normalises it again to 1. An example of a

time-dependant component would be a cooling sensor, which is only un-

dergoing wear when subjected to cooling.

3) Load-dependant components: The war of load-dependant components

can be calculated using the actual chronological loading during the pro-

cessing. For rolling bodies such as ball screws, rolling bearings, linear

guidances the lifetime calculation (according to DIN ISO 3408, DIN ISO

281 and DIN ISO 14728) can be transformed and split into single loading

terms. Hence the incremental load of the momentary strain can be calcu-

lated as a defined wearout share of a total load for a given total lifetime.

Figure 6 Approach model for load oriented maintenance planning rules

The derived load oriented planning rules are combined together with quality

oriented planning rules in the reaction model in order to ensure a holistic approach

for maintenance planning.

6.0 Conclusion and Outlook

The key of the presented approach is to build conclusions based on the interaction

between condition- and load data, historical quality- and machine data as well as

the medium-term planning of production. With such an anticipatory maintenance

strategy it is possible to achieve the best possible product quality, optimized plant

availability and reduced maintenance costs. The methodology is going to be

applied in a maintenance control center for production lines in order to anticipate

failure and reveal deviations in quality on real-time basis.

Such a maintenance control center allows the human to make the final decision in

a complex environment. In this context it is important to mention that since the

Product ion

programme

Correlat ion between product ion programme, failure and qualit y ef fects

M aschine

cont rol

data

Failure and qualit y

ef fectsCategorizat ion of

maschine part s

Switch-dependantcomponents

Load-dependantcomponents

Time-dependantcomponents

322

operator is able to apply his implicit knowledge not only the quality of decision

making will increase, but also the acceptance of the planning support will grow

[14].

The linkage of machine, product and process perspective facilitates a change in

thinking. We need to step away from the out-dated motto “What are the costs of

maintenance?” towards a new perspective: “What are the costs maintenance is

able to prevent?” [1].

Acknowledgement

The methodology regarding the holistic maintenance approach mentioned above

has been developed within the research project “Maintenance 4.0”, funded by the

Austrian Research Promotion Agency (FFG), Grant number 843668.

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Authors’ Biography

Robert Glawar

Dipl.-Ing. Robert Glawar is a researcher at the Institute

of Management Science of the Vienna University of

Technology since. His main field of research is the

analysis, optimization and implementation of processes

with focus on maintenance planning. He contributed in

numerous industries funded applied research projects

dealing with the optimization of production planning

processes as well as the optimization of ordering

processing.

Christoph Habersohn

Dr. Dipl.Ing. Christoph Habersohn is since 2006 a

research associate at the Institute of Production

Engineering and Laser Technology. Main research

activities are in the fields of automation technology

regarding to the machine control and sensor layer;

construction of special purpose machines for advanced

production technologies like vibration assisted

machining; machine tool measurements and verification.

324

Tanja Nemeth

Dipl.-Ing. Tanja Nemeth is research assistant at the In-

stitute of Management Science of the Vienna University

of Technology since 2013. Her research focus is on holis-

tic maintenance planning and scheduling as well as the

analysis and optimization of production related process-

es. Currently, she is working on the national funded re-

search project “Maintenance 4.0”.

Kurt Matyas

Univ.-Prof. Dr. Dipl.-Ing. Matyas, is professor at the

Institute of Management Science of the Vienna

University of Technology (TU Wien) since 2001.

Currently he is Vice Rector for Academic Affairs at TU

Wien. His research and consulting topics cover

production management, logistics, and maintenance.

Burkhard Kittl

Univ.-Prof. Dipl.-Ing. Dr. Burkhard Kittl is associate

professor for Computer Aided Manufacturing at the

Institute for Production Engineering and Laser

Technology of the Vienna University of Technology (TU

Wien). Main research topics are Manufacturing

Execution Systems and their integration with monitoring

and control level.

Wilfried Sihn,

Univ.-Prof. Prof. eh. Dr.-Ing. Dr. h.c. Dipl.-Wirtsch.-

Ing. Sihn is Professor at the Institute of Management

Science and currently head of the institute. Prof. Sihn is

Director of Fraunhofer Austria and has been active in the

field of applied research and consulting services for more

than 25 years now.