توقف بهینه سازی زمان در نظارت بر وضعیت
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|21707||2002||7 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Reliability Engineering & System Safety, Volume 76, Issue 3, June 2002, Pages 319–325
Automated condition monitoring of active components of a system can improve the cost-efficiency of preventive and corrective maintenance and the availability of the production system. The validity, reliability and correct interpretation of the signals obtained from the condition monitoring instrumentation is important for the realisation of the potential benefits. The utilisation of experts in the interpretation of the condition monitoring signals is therefore crucial. In the paper, a stopping time model is formulated, where experts' judgements on the remaining operating time of a component, given an indication of incipient failure, are utilised to arrive at optimal operational maintenance decisions. Optimality is defined in the sense of maximising expected utility. An expert model is also formulated, which utilises percentile information elicited from the experts. The modelling framework allows for the testing of different modelling assumptions which affect the decision outcomes.
Short-term operational maintenance decision-making based on condition monitoring has to take into consideration the predictive power of the observations made. Basically, observations can be made through process and condition monitoring. Especially, automated physical measurement of the condition of equipment has lately been increasingly implemented for predictive maintenance. In this case, the validity, reliability and the correct interpretation of the readings of the sensors are essential for the benefits of condition monitoring to realise. The signal validation project  in the Halden Reactor Project, develops on-line methods to verify the correctness of the signal received from the sensors. Wrong judgements from inspections and tests, or wrong interpretation of measurements due to faulted or miscalibrated instrumentation might lead to operative decisions, which are non-optimal in terms of costs due to excessive production losses or repair. Operational maintenance decisions also have safety implications in many cases, stressing the importance of proper decision-making. In the case of automated condition monitoring, we are confronted with the operational maintenance decision problem of continuing operation with a degradation in the active equipment or component, given that an indication of an incipient failure has been received from the sensors. We can distinguish between outcomes of a certain operational maintenance decision as shown in Table 1. The basic temporal realisations of the functional breakdown of a component, denoted by t1 and t2, are shown in Fig. 1together with the time points related to the decision options. The earlier described short-term evaluation of the decision outcomes does not take into account the long-term ‘costs’ of the different outcomes on safety and work culture such as compliance with safety rules and adherence to the quality of maintenance work. These long-term aspects have to be considered in the decision-making also and are usually expressed as design and operative constraints of systems . Ideally, we can argue that from the point of view of the owners of a production system, the optimal design of a system or a component is such that its lifetime coincides with the scheduled time for replacement or overhaul. Roughly speaking, a system or a component should be good enough to perform as planned (planned output/input and lifetime), but not better (waste of resources). By introducing proper condition monitoring, the production personnel/manager can obtain information about unexpected discrepancies in the condition of the components of a system and make risk-informed maintenance decisions given this information. The rationale is that the cost induced by implementing condition monitoring is out-weighted by the benefits of the information that it produces. The assessment of the validity of this rationale is a task related to long-term maintenance planning . In the following, we will utilise expected utility (EU) theory in the formulation of a stochastic stopping time model . The stopping time model is utilised for operative maintenance decision-making when an incipient failure has been detected. This entails the modelling of the decision-makers subjective risk-attitude, on one hand, and the modelling of experts' judgements on the failure time, given the incipient failure indication, on the other hand. Expert judgement will be used in a direct way: experts are asked to provide percentile information on residual lifetime of the component given the indication of incipient failure. Thus, the modelling will focus on the use of expert judgement rather than on physical degradation processes . The optimal maintenance decision in terms of the optional basic (generic) operational maintenance decisions in Table 1, will be concluded on the basis of the optimal stopping times derived from the stopping time model. Optimality is defined in the sense of maximising EU . In Section 2, the operational maintenance decision problem is formulated as a stopping time optimisation problem. Section 3 describes a probabilistic expert judgement model. An emphasis is put to modelling dependence between these judgements by specifying a probabilistic model based on the multivariate normal data model. Section 4 demonstrates the stopping time model through an example and interprets the results in terms of the basic operational maintenance decision options.
نتیجه گیری انگلیسی
A stopping time optimisation problem is formulated, where the role of expert judgements and the dependence between the judgements are focused on. Optimal stopping times are derived maximising EP and EU. A decreasingly risk-averse attitude of the decision-maker is studied. Experts' judgements about failure time are modelled using a multivariate normal data model. This allows for explicit specification of correlation between the judgements. The following effects on the decision criteria ‘EP’ and ‘EU’ and the optimal stopping times can be studied using the described stopping time optimisation model: • The effect of dependence between experts' judgements, specifically the uncertainty related to the correlation between experts' judgements based on the multivariate normal model; • The effect of the decision maker's risk attitude, specifically decreasing risk aversion; • The effect of the prior probability distribution of the failure time. The implications of the calculated optimal stopping time to the operative maintenance decision making has to concluded from a wider perspective than that of the stopping time optimisation model. For instance, how are maintenance resources available at the time prescribed by the model? Such considerations may influence the operative maintenance decision making as much as the results obtained from the stopping time optimisation model. The earlier effects are intertwined in a complex way. The most challenging and critical modelling task is the definition of the prior distribution of the failure time. Loosely speaking, the mean of the posterior distribution of the logarithm of the failure time (see Eq. (14)) is a weighted average of the prior mean and the experts' judgements on the median of the failure time. The weighting factors are proportional to the inverse of the variances and the co-variances. If the experts are few, vague and correlated, and the prior is informative (equal to high precision), the effect of the experts' judgements is easily muted. Therefore, it is recommendable to define the prior probability distribution to express a vague rather than too precise an information. The expert judgement model could be criticised for the use of expert judgements to specify the probability model related to the point estimate representing their ‘best guess’ of the unknown failure time. It is, however, the experience of the author that experts are more willing to express their judgements in a form that reflects their uncertainty on the outcome of an event rather than just point estimates. Therefore, expert judgement models that incorporate e.g. percentile information, such as the expert judgement model described in the paper, should be developed and used.