UPDATING A MAINTENANCE PROGRAMME
BASED ON VARIOUS INFORMATION
SOURCES
Ahonen T., Reunanen M., Heikkilä J., Kunttu S.
VTT Industrial Systems, P.O. Box 1300, FIN-33101 Tampere, Finland
Abstract: Optimised decision making in maintenance programme development can lead to higher
profitability. Different sources of data can be utilised in maintenance optimisation. Models for converting data into information and procedures for using this information in order to develop an efficient maintenance programme can be improved. An ongoing VTT project aims to develop procedures to combine the use of different information sources. This paper describes how the presented approach has been applied in a case study of the research project.
1. Introduction
Event data based prediction of the number of failures at a high system level seldom gives enough information in order to be able to make updates and adjustments to the maintenance programme. Instead of relying on the analysis of maintenance event data alone, the reliability risks of the system can be investigated in a more comprehensive way by using Failure Modes, Effects and Criticality Analysis (FMECA) results and event data together. By combining these sources of information, a comprehensive list of the most critical failure scenarios can be compiled. Often the material concerning failure events is scarce. This may be due to a limited time period to collect information or deficiencies in the practices to record all failures and the related maintenance actions. In order to overcome this problem, we propose Bayesian methods to be used in order to combine estimates based on expert judgement with several other data sources.
Identification of the problematic subsystems or components of the system is essential in order to reach a good understanding of the risks related to the failures. With risk analyses and analysis of failure event data it is possible to point out high risk items that can serve as development points for maintenance practices.
According to IEC-60300-3-11 [1] the maintenance programmes are composed of an initial programme and an on-going, dynamic programme. The initial maintenance programme or
recommendations regarding the maintenance tasks are often delivered by the manufacturer. The purpose of our research has been to find practical means to develop and update a dynamic maintenance programme.
2.
An approach to make updates to a maintenance programme using
various information sources
The aim of our research is to find methods to identify development points in a maintenance programme and to make updates based on the results of the identification and the related analysis tasks. Figure 1 gives an overview of important factors that need to be considered in the maintenance programme development and updating stages.
Data combiningData combining Objectives and boundary conditions of the system
Structure and function of the system
Predicted duty type and production volume
Maintenance actions Event data Measurement data
Machinery manufacturer's recommendations Maintenance programme New predictions Updating
Fig 1. The use of different information sources in the maintenance programme development and updating stages
IEC-60300-3-11 [1] presents a method for developing an initial maintenance programme. The standard does not include detailed guidelines on how to develop and update an on-going maintenance programme. Moreover, carrying out a full and systematic RCM study is often claimed to require a lot of time and resources, especially in a situation where good candidate maintenance programmes are available. These are often prepared based on the experience of the manufacturer or the user.
We propose that updating the maintenance programme includes the following phases: 1. The analysis of the differences between the maintenance programmes of the
manufacturer and initial implemented maintenance programme. As a result we get candidate programme elements for further study.
2. Identification of the missing aspects and later the changes in the system and defining new maintenance tasks based on the application of the RCM methodology on the changed parts of the system.
This step is part of the continuous maintenance development using different information sources and taking into account the progress of failures.
The initial maintenance programme or recommendations regarding the maintenance tasks are typically delivered by the manufacturer. The actual implementation is often somewhat different. The analysis of the differences and their causes can be used as one source in maintenance programme development. In order to be able to develop effective preventive maintenance tasks, the analysis should include a discussion on the reasons for the differences. The drawback of the comparison is that it pays attention to only those programme items that are included in the documents that are compared. Potential missing items are not covered by the comparison.
The comparison should cover the following aspects:
the tasks listed in one programme but not in the other, differences in comparable tasks,
differences in scheduling.
The importance of the tasks appearing only in the manufacturer's maintenance programme should be evaluated based on the analysis of event data as well as the use and maintenance experience. The tasks developed by the users and maintainers are often justified by experience. The experience on the success of the tasks, on the other hand, can often be used to optimise the scheduling.
According to IEC-60300-3-9 [2], Failure Modes, Effects and Criticality Analysis (FMECA) is used to analyse all the fault modes of the equipment item for their effects on other components and the system. In the proposed approach, the FMECA study can be used to produce information on new failures that have not been covered by the programme documents compared in the previous phase of the approach. The application of the RCM decision logic tree, described in IEC-60300-3-11 [1], can be used to develop preventive maintenance tasks for the new fault modes identified by the FMECA study.
Event data can be used to identify the critical points and reliability changes in the system as well as to estimate the failure rates and component lifetimes. When making estimates of the remaining lifetime of a component, the first estimate can always be made based on expert judgement. The result can then be updated with event data. The approach, discussed further in Section 3, can also be used for determining maintenance schedules of components and equipment.
3. Using event data with expert judgement to support maintenance
decisions
In practical case studies, the recorded maintenance event data is typically scarce, and expert judgement is typically used to overcome this problem. Bayesian approaches can be
used to combine the data with expert judgement for example. If using data from different sources, also the fitness of the data need to be confirmed.
Kunttu and Kortelainen [3] remind that prediction of the number of failures at a high system level may not be sufficient because it does not give any information on which components are responsible for the failures. Even calculating the average values of component lifetimes does not fulfil the need for information in maintenance planning. The assessment of the distribution, based on the data and expert judgement, can be used better to generate background information. The probabilistic approach is seen as a good means to represent the information available with the uncertainty attached to it.
Brandowski and Grabski [4] have proposed a Bayesian non-parametric approach to present the reliability characteristics based on expert judgement and event data. The estimates elicited from experts can be used to evaluate the reliability function but as information of the failures of the component is collected, the estimated function can be updated. The estimation of prior reliability function is made based on Weibull distribution. The updates with data are made based on Dirichlet process. The experts are asked to estimate the time t1 for which they have p1 % confidence that the event {X<t1}
will occur. Opinion on time t2 for which they have p2 % confidence that the event {X<t2}
will occur, is also asked. In the example presented by Brandowski et al. [4] the probability figures are selected as follows, p1=0.5 and p2=0.99. In our case example, similar selections
were made. The numbers t1 and t2 are used to get the parameters γ and λ of the Weibull
distribution F(t) 1et :
)
t
/
t
ln(
)
p
1
ln(
/
)
p
1
ln(
ln
2 1 1 1
,
1 1t
)
p
1
ln(
. (1) The estimate of the reliability function based on expert judgement can then be updated with the recorded failure data according to equation 2:
,t
n
r
1
e
w
n
r
r
)
X
,...,
X
|
t
(
R
n 1 i Xi r 1 k k t k k n 1 (2) where: r - number of experts, n - number of observations, wk - weight of each expert andXi - Dirichlet process parameter for each observation of the time to failure,
t
,
1
i
X
Figure 2 gives an example of the application of the method. The data used in the method must be in the form of time to failures concerning the component covered. Uncensored data is assumed.
Fig. 2. An example of a cumulative failure probability function
4. Case study
The approach described in Sections 2 and 3 has been used in updating a maintenance programme of a loader used in mining applications. The practical work was carried out in well-planned workshops, where various sources of information, that had been prepared beforehand, were discussed. These included the following documents:
maintenance programme suggested by the machinery manufacturer, the initial implemented maintenance programme,
analysis of the differences in the two versions of the maintenance programme, a high level FMECA study and the application of an RCM decision logic for the most
critical failures of the system,
analysis results of the event data.
On the practical side it was important to focus on new technology and changes in the system from the point of view of maintenance. The system can encounter different kinds of changes but new technology poses the biggest challenge for maintenance. On the other hand, most critical failures will entail the replacement of certain components. By modelling the lifetime with certain assumptions, deterioration of the components is taken into consideration. The deterioration of the system as a whole was not seen in the failure rate based on the total number of failures but it was shown that during the lifetime, the focus areas of the failures do change. The data analysis showed that the failure modes of the system can be somewhat different in the different phases of the loader's lifetime and the maintenance work should be designed to take the change into account.
The failure recordings in the case study were partially inadequate for reliable estimates of the unavailability of the system. Expert judgement was used to supplement the data. The
downtime caused by each failure should be recorded but this was poorly done in this case. The procedures of risk analysis and event data analysis should be based on a uniform subsystem classification. It was shown that the rough classification initially used in this case study was inadequate to support the decision making in maintenance and a component level examination was justified. The number of failures per unit time can be calculated and predicted for each subsystem. The reliability function can be calculated based on expert judgement and data for any component. Figure 3 shows the reliability functions for two transmission components.
The data used in this case study was originally in free form and classified during our research. Through the classification the data was transformed to meet the requirements of the method described in Section 3. Several failures of the components were wear-out-failures. The probability of those failures increases with the operating time. In the case study, the modelling of component lifetime was also made on the basis of the operating time.
Fig. 3. The cumulative failure probability functions of two transmission components A criticality assessment where the number of failures is based on event data is shown in Figure 4. The cost of different failure types is calculated based on expert judgement. Especially with respect to rare failure events, a criticality assessment based on event data alone may be incomplete. A more representative assessment of failures in the system can be obtained by also incorporating the FMECA results. Criticality assessment was also used to prioritise the results of the maintenance programme comparison.
Fig. 4. Criticality assessment of components and subsystems based on event data
The criticality assessment suggests that the transmission was the most critical subsystem. The focus should be centred on developing preventive maintenance tasks specifically for axle components.
Oil analyses are typically utilized in the condition monitoring and decision making concerning axles and other transmission components. Therefore, the results of event data analysis and reliability function estimation were supported by the oil analysis results from different components. The use of expert judgement and event data can be seen to support the long-term maintenance development. Measurement data analysis and condition monitoring can ideally be used in short term to update the estimated lifetime and to support situation specific actions.
In order to develop a comprehensive maintenance programme, all the necessary documentation needs to available at the expert workshop to promote constructive discussion. The participants should include:
reliability and condition monitoring experts, maintenance staff,
the users,
manufacturer(s).
The maintenance development procedure was carried out in two stages: in the first workshop the general view of the maintenance programme was covered. (ie. focusing on the long-term planning of preventive maintenance). In the second workshop, the daily preventive maintenance tasks and inspections carried out by the loader users were covered.
1. The first expert workshop consisted of the following topics:
an overview of the failure behaviour of the system,
comparison between the different maintenance programme versions (schedules for
a subsystem-specific analysis of the maintenance tasks that came up during the
maintenance programme comparison and decision making concerning each task's importance and need for emphasis,
the effects of new technology on the maintenance tasks and scheduling for each subsystem,
the new maintenance tasks related to the new technology based on experiences from
similar systems as well as instructions and recommendations from the manufacturer. 2. The second workshop consisted of the following elements:
an overview of the present situation of the daily preventive maintenance,
evaluating the need for new daily inspections and estimating the efficiency of the present tasks performed by the users.
As a result of the application of the approach the maintenance programme was examined and the emphasis on different tasks evaluated. The criticality assessments described above were used to direct the maintenance efforts where needed most. The quantitative analysis used in the case study and described in Section 3 on the other hand produces background information about component lifetimes for maintenance planning.
5. Conclusions
Designing a dynamic maintenance programme can be a demanding task. All the aspects are seldom taken into consideration and therefore it is important to focus on the aspect and information sources that are most essential.
Based on experience, it is typically not sufficient that event data alone be used to predict failures and evaluate the reliability of the system. Therefore, the use of different sources of data is needed in producing information to support maintenance programme development and the actual maintenance decisions. A practical model is needed for using the sources optimally together. The model proposed is a combination of quantitative methods of data analysis and qualitative methods of risk analysis and maintenance planning. The research on quantitative methods has lead to the comprehension of the fact that the collecting, pre-processing and categorization of the event data is often the main problem and that the use of sophisticated methods are often limited by poor or insufficient data. The case study showed that with a rather simple and easily implemented method one is able to get and present the needed information on certain components. The total number of failures is typically easily available but the need for information is usually on the number of failures concerning a certain subsystem and failure type. A reliable estimation of the total number of failures and the availability of the system, for instance, can be made based on the estimations made at lower system levels.
References
1. IEC-60300-3-11. Dependability management – Part 3-11: Application guide. Reliability centred maintenance. International Electrochemical Commission IEC, p. 90.
2. IEC-60300-3-9. Dependability management – Part 3: Application guide. Section 9: Risk analysis of technological systems. International Electrochemical Commission IEC, p 47.
3. Kunttu S., Kortelainen H.: Supporting Maintenance Decisions with Expert and Event Data, Proc. Ann. Reliability & Maintainability Symp., January 2004, pp.593 – 599. 4. Brandowski A., Grabski F.: Bayesian estimation of the parameters in safety and
reliability models for the subjective priors. European Safety and Reliability Conference (ESREL 2005), June 2003, pp. 255 – 260.
