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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. David.baglee@sunderland.ac.uk
VTT Technical Research Centre of Finland.Erkki.Jantunen@vtt.fi

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
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Services. under responsibility of the Programme Chair of the 3rd InternationalThrough-life Engineering Conference
Peer-review

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
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Programme Chair of the 3rd InternationalThrough-life Engineering Conference
doi:10.1016/j.procir.2014.07.003

3) Gradual wear out after long useful life (Fig. (2013). 2. The functions that with regard to the degradation behavior of the equipment. In addition. 2. 2 and 3) can be monitored. 1.g. In order to identify the financial and productivity benefits from a CBM strategy it is Fig. sudden/random failure of recently were either unaware of the benefits or unsure on how electronic components). Naturally. 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. Nevertheless. 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. Condition Monitoring tools have proven successful in reducing unplanned downtime by preventing equipment or process failure. to best make use of the equipment. 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. 4). one breakdown can affect the entire production process. demonstrating the magnitude of the savings that can be generated using CBM is difficult. in order Fig. rapid wear out (Fig. Gradual wear out.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. size impractical to monitor the remaining three (4. 1). CBM Modelling 2) Rapid wear out after long useful life (Fig. However. (2010) and more recently in Arnaiz et al. useful life. 2). necessary to start with a detailed range of functions from . It As the gathered data clearly shows differences between is important to use an appropriate method for modelling various industrial sectors do exist. 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. Rapid wear out after long useful life. 3. infant mortality. findings deterioration to identify the different conditions and their outline a need for accurate data to support similar studies. 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. This is often due to lack of understanding implement specific maintenance actions. 5 and 6) as reductions. critical equipment is available when required. 1) Bathtub curve. A complete CBM system comprises a number of functional capabilities including a range of sensors and data acquisition techniques. 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. Theoretically modern maintenance technologies have relatively short there are different types of failure characteristics often payback time. 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. the importance and understanding of various failure models before embarking on a condition based maintenance strategy. costs. improve equipment availability and ensure that mission 5) Indefinite useful life (constant failure rate) (Fig. 2013): industrial sectors where the production forms a chain i. capabilities of predictive maintenance technologies have increased in recent years with advances made in sensor The first three (1. First attempts in this Fig. Modeling to identify deterioration is often overlooked. In effects. the highest benefits can be gained in grouped in six categories (Tutorial Part 14.e. (2010) and Fumagalli et al. Bathtub curve: Infant mortality – useful life – rapid wear out. The diagnostic 6) Infant mortality followed by indefinite useful life (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. These advances in component sensitivities. whereas it is technologies. 3). 6). direction have been provided by Jantunen et al. 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. 5).

where and how”. in % useful life. David Baglee and Erkki Jantunen / Procedia CIRP 22 (2014) 87 – 91 89 3. 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. in certain situations. The interviews allowed a range of experiences. after long after long followed by life. when. sectors in different countries. varied and ‘uncensored’ view of maintenance strategy development. 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 . situations and knowledge that would otherwise be hidden. The questions were similar to each organisation i. Data analyses Industrial Country Bathtub Rapid Gradual No infant Indefinite Infant Logical sector curve. 4. “who. useful useful indefinite indefinite rapid wear life. The results are shown in table 1 below. maintenance professionals from companies who provided maintenance consultancy were contacted. from 5 different countries. For example. The aim was to collect. to be discussed and analysed. 6. 5. Infant mortality followed by indefinite useful life. Interviews were carried out with senior managers while questionnaires were distributed to shop floor personal. to support the views of senior management. Table 1. table 2 shows the mortality and how CBM could be sued to support comparison of the process industry data. questionnaires. Data collection Data were collected using interviews. In addition. and in many situations dispute the views of senior managers. analyse and present a Fig. (presented in table 1) maintenance decisions. Indefinite useful life. Fig. infant wear out wear out mortality useful mortality to use mortality. examining their maintenance practices. in % life. scientific company profiles from approximately 60 companies from 12 countries. in % followed by CBM useful life. The questionnaires were used. what. Although the interviews were open they did provide a systematic description on: x Their current maintenance practices. x Their justification for using this maintenance method.e. useful life. x How management decisions are taken when Fig. No infant mortality followed by indefinite useful life.

the figures which represent gradual wear out after long life supply chain would benefit from CBM. long useful useful life in indefinite indefinite useful use CBM wear out % in % life.5 million validity of the data supplied by the respondents. This firstly questions Toyota.35%. 60% of the respondents stated that they suffer CBM. In an industry hydraulic pitches or bearing systems. 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. In Spain. 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. which includes Nissan. USA aerospace industry claim CBM is needed. suggest that the industry could benefit with a wider uptake of In the UK. form and assemble small fixtures. Comparison of the process industry data from 5 different countries. In these components which is highly regulated and components are made to exact . in addition it cars produced in 2013. Indefinite out after out after long followed by followed by Logical to Country useful life. of ineffective maintenance practices. table reports. This is true on assembly lines operated by robots where from infant mortality with useful life and rapid wear out. No infant Rapid wear Gradual wear mortality Infant mortality Infant mortality. or the data collected is according to the manufacturing companies surveyed. about inaccurate and not be validated. are within 1%-5%. in % % useful life.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. the importance of the wear failure models regarding If we return table 1. rapid useful life. This the majority of robots weld.e. This. is increasing with approximately 1. This is contrasted by the use of CBM Automotive production in the UK. the practices. This raises the Little or no monitoring of robots takes place. it is interesting to note that 11% of the mechanical components. as with the UK suggests they employ a range On the other hand. gear boxes. Honda. figures which fall within 90% . such as spindles. The data presented in the table respondent countries. 65% of robotic systems have an infant mortality The figures for Belgium indicate that they suffer from followed by indefinite useful life. This is an interesting claim inefficient maintenance practices across the range of and one which should be examined by the UK auto-industry. high figure seems to be unique to the UK. This is an area question of the type and efficiency of current maintenance of great interest to the UK auto-manufacturers. categories. in life. there might be some consensus.

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