Proceedings of the Institution of Mechanical Engineers,Part O: Journal of Risk and Reliability

The study deals with a gradually deteriorating system such as a large structure. This system is studied over a finite time span where the finite horizon can be seen, for example, as an insurance deadline which requires a specific maintenance policy. Maintenance actions are assumed to be imperfect in this work. An improvement function is used to model the impact of the maintenance on the degradation level of the system. The improvement function is based in the virtual age model ARA ₁. A maintenance policy is then proposed in which maintenance actions are systematically performed at given maintenance dates, if the system has not already failed. It is assumed that in the event of a failure the system is not repaired. The system is then unavailable until the finite horizon. The proposed maintenance policy is assessed on the finite time span, and both maintenance dates and the number of maintenance actions are optimized.

A large number of maintenance models are available in the literature. Most of these usually assume that the effect of maintenance interventions is as good as new. This amounts to assuming the maintenance action undergone by a system, be it preventive or corrective, is equivalent to its replacement. This hypothesis is, of course, questionable in many cases. Maintenance without replacement can lead to a significant level of rejuvenation of a system, either preventively or after repair. However, the restoration of the performances of the system is most of the time incomplete. The effect of such an imperfect maintenance has been described in different ways, which can be split in two main categories: reduction of the value of a degradation variable embodying the ‘health’ of the system, or modification of the lifetime distribution of the system.

This paper focuses on the latter approach, and is structured in two parts. First, it reviews different approaches of imperfect maintenance, modelling the gain in residual lifetime either by a decrease of the failure rate value or by a reduction of the system effective age. Second, an innovative model based on the concepts of elasticity and inescapability of aging is described, in order to introduce more intuitive observations on the results of repeated maintenance actions on a system. This new approach is illustrated using a numerical example.

The formulation of a partially observed Markov decision process (POMDP) model to adaptively schedule testing, minimal repairs and overhauls for a two-state process with age-dependent degradation is presented. Structurally, the optimal maintenance policy is of control-limit type with respect to the repair and overhaul actions in both age and the probability of being out-of-control. A tailored solution algorithm is developed that iteratively determines an upper bound on the truncation age under the optimal policy. Numerical examples highlight the flexibility of the model, possible complexities of the optimal policy, and cost savings in comparison with less flexible models.

In this paper, a modelling approach is presented to assess the performance of multicomponent systems maintained by complex maintenance strategies. A global framework is built to describe the evolution of the system, the way its components can degrade and fail, and the effects of different maintenance actions. A two-level modelling framework makes it possible to describe fully the entire causal chain that can lead to system dysfunction and the possible maintenance tasks of the reliability centred maintenance (RCM) method. A cost model is defined to assess system performance in terms of cost and unavailability. The main originality of this work is that the complexity of both maintenance programmes and system structures is taken into account. The model can be used as a decision-making tool to choose from a selection of various maintenance options. Promising results have been obtained in case studies based on systems from nuclear power plants.

This paper considers a binary monotone system consisting of n components. The system failure process is given by a counting process whose intensity at all times is adapted to information about the system state and at periodic inspection times it is adapted also to information about the component states and their underlying conditions. A parametric policy is introduced prescribing replacement of the entire system either when it enters the failed state, which is observed immediately, or when upon an inspection the intensity is found to have reached a predefined threshold. Costs are associated with inspections, replacements, and system failures, and the periodic inspection interval and intensity threshold that jointly minimize the expected long run cost per unit of time are determined. A numerical example is also included.

This work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation.

The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts.

Maintenance policies include break-down-based maintenance, time-based maintenance, and condition-based maintenance. The advances in condition monitoring techniques have made condition-based maintenance a popular and increasingly important choice. With the increased use of condition monitoring information, there is obviously a need for appropriate decision support in plant maintenance planning utilizing available condition monitoring information. However, compared with the extensive literature on diagnosis, relatively little research has been done on the prognosis side of condition-based maintenance. In plant prognosis, a key, but often uncertain, quantity to be modelled is the residual life prediction based on available condition information to date. This paper overviews a semi-stochastic filtering-based residual life prediction approach for the monitored items in condition-based maintenance and introduces the associated applications. First the role of residual life prediction in condition-based maintenance decision making is demonstrated, which highlights the need for such a prediction. Then a detailed discussion is presented of the semi-stochastic filtering models developed for residual life prediction, the extensions made, and the case applications applied to. Finally the results of a comparative study between the semi-stochastic filtering based model and another popular model using empirical data are briefly given. The results show that the filtering-based approach is better in terms of prediction accuracy and cost effectiveness.

This paper deals with the preventive maintenance (PM) optimization of air-conditioning systems used aboard regional trains in France by the SNCF (French Railway Company). Two kinds of PM policies are envisioned: one with a single overhaul in the whole lifetime of the air-conditioning system, another with opportunistic replacements of components that are too old at each system failure. The air-conditioning system is formed of about 20 ageing and stochastically independent components. The envisioned PM policies make them functionally dependent, however. Both PM optimizations are performed with respect to the same cost function, involving the mean number of component replacements on some finite horizon. In view of its numerical assessment, a piecewise deterministic Markov processes (PDMP) model is used, both to model the maintained and the unmaintained system; a deterministic numerical scheme is next proposed, based on finite volume (FV) methods for PDMPs; owing to difficulties in its implementation, an approximation of this scheme is next used, which is much easier to implement than the initial FV scheme. As a result of using this method, it was finally possible to optimize both PM policies, which are both proved to lower the cost function of about 7 per cent.

Manufacturers are currently carrying out two effective strategies to reduce the warranty servicing cost of products with bathtub shaped failure rates. The first strategy is the burn-in procedure when products are operated for a reasonably short period of time prior to usage. This strategy can be effective when the initial failure rate is strictly decreasing (infant mortality). The second strategy is preventive maintenance actions at discrete time instances over the warranty period which can effectively reduce the age of the item when the failure rate is strictly increasing (wear-out). In this paper, the optimal burn-in time and imperfect preventive maintenance strategies for a warranted product with the bathtub-shaped failure rate are investigated. We provide a numerical study to illustrate our results.

Life extension has for a long time been an important and highly discussed issue in nuclear and aviation industries, and has recently attracted considerable attention in the subsea oil and gas industry. Decision-making related to life extension is a multidisciplinary problem, but it primarily depends on the remaining useful life. This paper clarifies the concepts of remaining useful life and technical health, and discusses various influencing factors. An overall model with the capability to handle a heterogeneous combination of requirements, such as degradation modelling, uncertain environmental and operational conditions, uncertain sensor data and expert opinion is suggested for life extension decision-making. It is concluded that a physics-based modelling approach is appropriate for equipment in the subsea oil and gas industry.

Large groups of structures like bridges, pavements and sewer systems, are often inspected visually and their condition is quantified based on a discrete scale which results in categorical data. Markov chains have traditionally been used to model the uncertain rate at which these structures progress through such a condition scale. In order to determine optimal strategies for inspections and maintenance activities, these Markov chains must be fitted to the data obtained in the field. This paper presents details on how to implement the method of uniformization for the calculation of interval transition probabilities and their sensitivity towards model parameters. The primary benefit of uniformization is that it provides a robust and efficient method for use in maximum likelihood estimation of transition probabilities or intensities in Markov chains.

The performance and reliability of engineering systems and structures are usually affected by uncertain degradation that occurs in service as a result of various physical and environmental processes, such as corrosion, erosion, fatigue, and creep. To maintain reliability of degrading systems, periodic inspection and preventive maintenance programmes are adopted. In the literature, the optimization of a maintenance programme is typically based on the minimization of the asymptotic cost rate. However, many engineering systems operate in a relatively short and finite time horizon in which the application of the asymptotic approximation becomes questionable. This paper presents an accurate formulation for computing the expected value and variance of the cost of a condition-based maintenance programme over a defined time horizon. A stochastic gamma process is used to model uncertain degradation. This paper emphasizes that the consideration of variance of the cost is of utmost importance in maintenance optimization, because it helps to identify a more robust (less uncertain) solution in a set of competing optimum solutions based on expected cost.

Warranty extensions are considered for automotive vehicles. In particular, the expected cost to a manufacturer of an extension to a base warranty is determined, while taking account of the effect of services during the warranty period. To model the effect of services, an inspection maintenance model is used that is based on the delay time concept. Therein, failures are preceded by defects, and the inspection of a vehicle while in the defective state facilitates the correction of such defects. Thus the typical circumstances in which a warranty places certain requirements on the customer to carry out a standard level of servicing is modelled. A vehicle is regarded as a complex system, with defects arising according to a counting process. Other defects that are present are allowed to be corrected when a vehicle fails. A case study carried out for Malaysian Truck Berhad is used to illustrate these concepts.