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Procedia CIRP 22 (2014) 87 – 91

3rd International Conference on Through-life Engineering Services

Can equipment failure modes support the use of a Condition Based
Maintenance Strategy?
David Baglee, Erkki Jantunen
University of Sunderland. UK.
VTT Technical Research Centre of

Over recent years, the importance of maintenance, and therefore maintenance management within manufacturing organizations has grown. This
is a result of increasing pressure upon manufacturing organizations to meet customer and corporate demands, and equipment availability and
performance is central to achieving these. Condition Based Maintenance (CBM) is widely accepted and used as a financially effective
maintenance strategy. The economic benefit of CBM is achieved if the tools and techniques associated with CBM are applied to the right
equipment. In particular the degradation behavior of the equipment needs to be understood. Understanding of degradation is strongly related
with failure models However, very little is known or published about the importance and the role of various failure models. Thus, if failure
models are not analyses and understood the use of CBM could be directed to the wrong equipment and therefore achieve incorrect and
expensive results. The paper examines the relationship between the failure patterns observed in industrial maintenance practice and the
corresponding impact on adoption and potential benefits of Condition-Based Maintenance (CBM). The paper will explain the need for accurate
and up to date equipment information to support the correct maintenance approach. The paper suggests the importance of further supporting
such investments by appropriately addressing the need to collect relevant data as a basis upon which to make the right decisions.

© 2014
© 2014 The
The Authors.
Authors. Published
Published by
by Elsevier
Elsevier B.V.
B.V.This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of the Programme Chair of EPSRC Centre for Innovative Manufacturing in Through-life Engineering
Services. under responsibility of the Programme Chair of the 3rd InternationalThrough-life Engineering Conference

Keywords: Condition Based Maintenane, Maintenance Efficiency

1. Introduction comprises a number of functional capabilities: sensing and
data acquisition, signal processing, condition and health
Maintenance engineering represents an area of great assessment, prognostics and decision aiding. Companies are
opportunity to reduce cost, improve productivity and increase moving from traditional corrective and preventive
profitability for manufacturing companies throughout the maintenance program to CBM to reduce the maintenance cost
world. There are examples of best practice what we may call and unnecessary maintenance schedules. A CBM program
World Class Maintenance which delivers great benefits. In consists of three key steps [1]:
addition, the maintenance of the infrastructure of modern
industry has become an increasingly important, and complex, x Data acquisition, to obtain data relevant to the system
activity – particularly as automation increases and the global health
marketplace in manufacturing squeezes profit margins. The x Signal processing, to handle the data or signals collected in
opportunity exists for many companies in Europe to see step 1 for better understanding and interpretation of the
substantial improvements to their competitiveness and data
profitability by improving their maintenance performance. x Maintenance decision making, to recommend efficient
Condition monitoring of plant and equipment has now been maintenance policies based on diagnosis and prognosis
identified as a major technique in establishing the optimum extracted from the data.
repair and maintenance periods to ensure in service reliability
and maximum utilization of assets. A complete CBM system

2212-8271 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of the Programme Chair of the 3rd InternationalThrough-life Engineering Conference

5). The diagnostic 6) Infant mortality followed by indefinite useful life (Fig. 3. the importance and understanding of various failure models before embarking on a condition based maintenance strategy.88 David Baglee and Erkki Jantunen / Procedia CIRP 22 (2014) 87 – 91 Unfortunately without a plan or path CBM strategies can which to collect and analyse data in order to develop and be unsuccessful. Bathtub curve: Infant mortality – useful life – rapid wear out. 3) Gradual wear out after long useful life (Fig. 2 and 3) can be monitored. capabilities of predictive maintenance technologies have increased in recent years with advances made in sensor The first three (1. First attempts in this Fig. 3). 6). 2013): industrial sectors where the production forms a chain i. In effects. In recent years there has been an increase in the use of 4) No infant mortality followed by indefinite useful life CBM as companies need to reduce maintenance and logistics (constant failure rate) (Fig. 5 and 6) as reductions. Rapid wear out after long useful life. costs. This is often due to lack of understanding implement specific maintenance actions. It As the gathered data clearly shows differences between is important to use an appropriate method for modelling various industrial sectors do exist. x signal processing and feature extraction This paper will present the results of a survey carried out x failure or fault diagnosis and health assessment with different maintenance experts to obtain information x identification of remaining useful life about failure models. In order to identify the financial and productivity benefits from a CBM strategy it is Fig.e. necessary to start with a detailed range of functions from . (2013). These advances in component sensitivities. one breakdown can affect the entire production process. rapid wear out (Fig. demonstrating the magnitude of the savings that can be generated using CBM is difficult. The functions that with regard to the degradation behavior of the equipment. Condition Monitoring tools have proven successful in reducing unplanned downtime by preventing equipment or process failure. 2). whereas it is technologies. findings deterioration to identify the different conditions and their outline a need for accurate data to support similar studies. Theoretically modern maintenance technologies have relatively short there are different types of failure characteristics often payback time. 4). 2. and the optimal selection and scheduling of the light of this study it is clear that all investments to support inspections and preventive maintenance actions. (2010) and more recently in Arnaiz et al. the highest benefits can be gained in grouped in six categories (Tutorial Part 14. size impractical to monitor the remaining three (4. useful life. 2. (2010) and Fumagalli et al. 1) Bathtub curve. critical equipment is available when required. in order Fig. This is due to internal accounting systems but mostly due to the inherent difficulties in estimating the often indirect positive impact that CBM has on savings. If facilitate CBM include but not limited to: failure modes are not identified and understood the use of CBM to support a maintenance strategy will be based upon x sensing and data acquisition false or inaccurate information. CBM Modelling 2) Rapid wear out after long useful life (Fig. to be successful in terms of cost to implement or equipment availability it is important to: a) Determine the cost of failures b) Determine the cost-benefit of avoiding failure. Nevertheless. Gradual wear out. In addition. improve equipment availability and ensure that mission 5) Indefinite useful life (constant failure rate) (Fig. This research activity aims at x management and control of data flows or test sequences encouraging the research community to examine and discuss x Modelling to identify deterioration. However. Naturally. to best make use of the equipment. A complete CBM system comprises a number of functional capabilities including a range of sensors and data acquisition techniques. sudden/random failure of recently were either unaware of the benefits or unsure on how electronic components). infant mortality. Modeling to identify deterioration is often overlooked. 1). and most importantly cost have opened up an there is no or little change that could be used to justify the entirely new area of diagnostics to the companies who until diagnosis of maintenance need (e. direction have been provided by Jantunen et al.g. 1. This requires detailed cost analyses of the current cost of maintenance and the necessary investment required to increase planned maintenance activities. This is achieved by providing asset managers with the information they need to implement real-time and need-based maintenance for deteriorating equipment.

in % life. The results are shown in table 1 below. sectors in different countries. varied and ‘uncensored’ view of maintenance strategy development. Infant mortality followed by indefinite useful life. infant wear out wear out mortality useful mortality to use mortality. No infant mortality followed by indefinite useful life. Interviews were carried out with senior managers while questionnaires were distributed to shop floor personal. David Baglee and Erkki Jantunen / Procedia CIRP 22 (2014) 87 – 91 89 3. from 5 different countries. and in many situations dispute the views of senior managers. Fig. in % useful life. x How management decisions are taken when Fig. “who. scientific company profiles from approximately 60 companies from 12 countries. out % in % in % Process France 30 % 30 % 30 % 3% 3% 3% 90 % industry Aerospace UK 10 % 10 % 70 % 0% 0% 0% 90 % Chemical Finland 10 % 10 % 70 % 0% 0% 10 % 90 % industry Mechanical Spain 10 % 30 % 50 % 0% 5% 5% 90 % components Tyre industry Russia 5% 10 % 70 % 5% 10 % 10 % 85 % Process UK 60 % 15 % 10 % 10 % 5% 5% 85 % industry Rail Spain 15 % 60 % 5% 10 % 10 % 0% 80 % Process Russia 10 % 20 % 50 % 0% 0% 20 % 80 % industry Mining Canada 30 % 20 % 30 % 0% 10 % 10 % 80 % industry Home UK 30 % 37 % 13 % 2% 0% 2% 80 % electronics Process Sweden 10 % 50 % 10 % 10 % 15 % 5% 70 % industry . in % followed by CBM useful life. to be discussed and analysed. (presented in table 1) maintenance decisions. The questions were similar to each organisation i. For example. in certain situations. when. x Their justification for using this maintenance method. The questionnaires were used. useful life.e. analyse and present a Fig. Although the interviews were open they did provide a systematic description on: x Their current maintenance practices. x The strategies employed to collect and analysed data It is immediately apparent that differences do exist to inform future maintenance between what the experts think regarding similar industry x Their understanding of useful and remaining life. 4. after long after long followed by life. 6. to support the views of senior management. In addition. The interviews allowed a range of experiences. examining their maintenance practices. where and how”. Data collection Data were collected using interviews. Indefinite useful life. 5. Data analyses Industrial Country Bathtub Rapid Gradual No infant Indefinite Infant Logical sector curve. maintenance professionals from companies who provided maintenance consultancy were contacted. useful useful indefinite indefinite rapid wear life. Table 1. situations and knowledge that would otherwise be hidden. The aim was to collect. table 2 shows the mortality and how CBM could be sued to support comparison of the process industry data. what. questionnaires.

This is a year by year increase of ap- is unclear if the same or similar equipment is used with the proximately 12% since 2009. gear boxes. the figures which represent gradual wear out after long life supply chain would benefit from CBM. This is true on assembly lines operated by robots where from infant mortality with useful life and rapid wear out. is increasing with approximately 1. categories. Honda.35%. as with the UK suggests they employ a range On the other hand. Comparison of the process industry data from 5 different countries. of ineffective maintenance practices. This is contrasted by the use of CBM Automotive production in the UK. which includes Nissan. 60% of the respondents stated that they suffer CBM. USA aerospace industry claim CBM is needed. in life. In an industry hydraulic pitches or bearing systems. In these components which is highly regulated and components are made to exact . figures which fall within 90% . about inaccurate and not be validated. table reports. such as spindles. This is an interesting claim inefficient maintenance practices across the range of and one which should be examined by the UK auto-industry.90 David Baglee and Erkki Jantunen / Procedia CIRP 22 (2014) 87 – 91 Electric Spain 5% 35 % 30 % 0% 30 % 0% 70 % motors/batteries Manufacturing Italy 5% 20 % 40 % 20 % 14 % 1% 65 % Mining Sweden 10 % 30 % 25 % 5% 20 % 10 % 65 % industry Lifts Spain 0% 35 % 30 % 0% 35 % 0% 65 % Robotic Spain 0% 30 % 30 % 0% 35 % 5% 60 % systems Manufacturing Spain 10 % 25 % 25 % 0% 30 % 10 % 60 % industry Machine tools Spain 10 % 40 % 5% 0% 40 % 5% 55 % Cars UK 10 % 21 % 22 % 10 % 13 % 14 % 53 % Paper industry Turkey 10 % 20 % 20 % 10 % 20 % 20 % 50 % Process Belgium 10 % 10 % 15 % 20 % 5% 10 % 35 % industry Mechanical Portugal 5% 10 % 15 % 20 % 25 % 25 % 30 % components Paper industry Sweden 4% 6% 15 % 18 % 20 % 37 % 25 % Ships USA 0% 17 % 0% 0% 42 % 29 % 17 % Aircraft USA 4% 2% 5% 7% 14 % 68 % 11 % Table 2. This. No infant Rapid wear Gradual wear mortality Infant mortality Infant mortality. Indefinite out after out after long followed by followed by Logical to Country useful life. in addition it cars produced in 2013. in % % France 30 % 30 30 % 3% 3% 3% 90 % UK 60 % 15% 10 % 10 % 5% 5% 85 % Russia 10 % 20% 50 % 0% 0% 20 % 80 % Sweden 10 % 50% 10 % 10 % 15 % 5% 70 % Belgium 10 % 10% 15 % 20 % 5% 10 % 35 % It is evident from the data that there are certain similarities tolerances it would follow that industries within the aerospace i. This raises the Little or no monitoring of robots takes place. it is interesting to note that 11% of the mechanical components. This firstly questions Toyota. there might be some consensus. The data presented in the table respondent countries. the importance of the wear failure models regarding If we return table 1. in % % useful life. suggest that the industry could benefit with a wider uptake of In the UK. the practices. or the data collected is according to the manufacturing companies surveyed.e. long useful useful life in indefinite indefinite useful use CBM wear out % in % life. form and assemble small fixtures. This is an area question of the type and efficiency of current maintenance of great interest to the UK auto-manufacturers. 65% of robotic systems have an infant mortality The figures for Belgium indicate that they suffer from followed by indefinite useful life. are within 1%-5%. This the majority of robots weld.5 million validity of the data supplied by the respondents. In Spain. rapid useful life. high figure seems to be unique to the UK.

prognostics and maintenance optimization. Fumagalli L. p. Portugal. An integrated confidently develop a set of failure modes. In mechanical failures are predominant. Di Leone F. In e- mining process would need to be developed in order to extract Proceedings of Intelligent Maintenance System. The conclusion is that there is an increasing and involving incorrect product usage increase the importance of rather urgent need for organizations to establish accurate indefinite useful life. Financial analysis different sectors. Advanced Maintenance Engineering. On the Need for Research on Holistic Maintenance. failure types seem to follow quite a different pattern in [6] Jantunen E. Helsinki. Services and observation made from the survey results is that the apparent Technology. and what would be the sectors. [3] Bengtsson M. informed choices regarding the introduction of CBM The next stage would be to use failure modes to categorise strategies. 2013. and Di Leone F.43. such on the basis of expert views. This would include expert perception of failures occurrences is that CBM has simple analyses to determine what could go wrong. To be published in Euromaintenance 2014. so as to facilitate more prevent. Ei-bar: Fundacion Tekniker.28.. aware of the importance of studying such failure statistics. However. and Dragan B. 15-17 valid. One Technologies in an eMaintenance platform. . Konde E. 2009. The aim is to evaluate process and manufacturing industries. However. Darning L. introducing a CBM strategy can be accessed on the face of [7] Jantunen E. Italy. Ramji A. Continuous Improvement on this study. 34 – Although the maintenance community has for long been 35. Issue 2. While this is so. A- MEST'10. p. such circumstances the decision can be out of necessity taken Wear mechanisms are also important in other sectors. [4] Fumagalli L.. and Salonen A. 2010. and Macchi M. and Fumagalli L. issue 2. comprehensible information from July. Industrial This paper presents a discussion of how different sectors maintenance management: a survey carried out on Italian companies. Conclusion Monitoring and Diagnostic Engineering Management (COMADEM). Information and On-line Maintenance Technologies for Increased Cost- effectiveness. Economic Value of different types of industries (Jantunen et al. p. previously unknown. 81. Procedia CIRP. Arles. The Asset Journal. of expert views regarding the type of failures occurring in [5] Fumagalli L. and Alarcón J. Andrews KSJ. Volume 7 pp. [8] Tutorial Part 14 (2013). Risk across the Life Cycle (continued). 38. Baglee D. Macchi M. and Macchi M. Reliability and Life Cycle Cost. this was References outside the scope of this initial investigation and requires in- depth analyses of the data and data sources to be able to [1] Lee J. In: 1st IFAC Work-shop. Proceedings of the 22th international congress on Condition 4. the current evidence from the the data into quantifiable problems. 5-8 May 2014. most typically in transport (aerospace. 2004 the organisations and individuals who supplied the data for [2] Arnaiz A. In: can benefit from the introduction of Condition-Based Proceedings of the 4th MM 2009 (Maintenance & Facility Management) Maintenance strategies. Identification of Wear evidence from occurring failure types across a range of Statistics to Determine the Need for a New Approach to Maintenance. why significant potential to bring substantial savings in different would the failure happen. A detailed data platform for diagnostics. with random failure events difficult to recording of the failure events. Risk. p. organizations. rail) and in consequences of each failure. in these two the need to be validated by actual observation in industrial increased product complexities and the process characteristics practice. Finland. 11. Lisboa. Jantunen E. Still such views would ideally as machine tools and lifts.87. Vladimiro C. Roma. France. Ierace S. 2014). Arnaiz A. The discussion is based on an analysis Conference. 193-198. 23. David Baglee and Erkki Jantunen / Procedia CIRP 22 (2014) 87 – 91 91 electronics are still kept to a minimum and therefore little data is available to enable a truly data-driven decision. MAINT-WORLD. It is evident that the cost-efficiency of of e-Maintenance. processes for possible failures and to prevent them by correcting the processes proactively rather than reacting to adverse events after failures have occurred.