Khac Tuan Huynh

Maintenance Decision-Making for Systems Operating Under Indirect Condition Monitoring: Value of Online Information and Impact of Measurement Uncertainty

This paper deals with maintenance decision-making for single-unit deteriorating systems operating under indirect condition monitoring. Based on a degradation and measurement model of crack growth propagation, two new maintenance policies using prognostic information are introduced. Their maintenance cost models are evaluated jointly by analytical and simulation approaches, and are compared with two more classical benchmark models. Such complete models integrating degradation phenomenon, monitoring characteristics, state estimation, prognostics, and maintenance assessment can give rise to fruitful numerical analyses and discussions. The main contributions of the paper are to i) analyze jointly the condition-based and dynamic structure of the considered maintenance policies; ii) propose some effective methods to reduce the effect of measurement uncertainty in condition-based maintenance decision-making; and iii) show the relevance of quantification methods when deciding to resort to prognostic approaches, and to invest in condition monitoring devices.

Multi-Level Decision-Making for The Predictive Maintenance of

Traditional maintenance decisions in the framework of condition-based maintenance applied to multi-component systems are performed either at the system level or at the component level. These decisions however cannot always assure the best maintenance performance. To remedy this drawback, the present paper introduces a multi-level decision-making approach that combines maintenance decisions at the system level and the component level. The effectiveness of such an approach is investigated through an n-component deteriorating system with a k-out-of- n:F structure, and economic dependence. In fact, based on the degradation and failure model of the considered k-out-of- n:F system, two new opportunistic predictive maintenance strategies with different types of maintenance decision-making are proposed. In the first one, the decisions rely only on the remaining useful lifetime of the components; while in the second one, they are based on both the remaining useful lifetimes of the system and that of its components. The maintenance cost models of these strategies are developed on the basis of semi-regenerative theory, optimized, and then compared with each other. The comparison results show that the multi-level decision-making approach allows us to more effectively avoid inopportune interventions, to better take into account the interactions among components, and hence to provide more flexible and profitable predictive maintenance strategies for multi-component systems.

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This paper provides a methodology to analyze the efficiency of mean residual life in condition-based maintenance decision-making. A degradation-threshold-dependent-shock model is used to describe the evolution of a system subject to the dependent and competing failure modes due to degradation and shock. Based on this model, we compute the mean residual life of system and analyze its monotonicity. This property of mean residual life function allows introducing a new condition-based maintenance strategy whose preventive maintenance decision is based on the mean residual life. The proposed strategy is then compared to a maintenance strategy based on the degradation level only. Analyzing the equivalence, the performance and the flexibility of both strategies allow us to give some conclusions on the interest of the mean residual life as a condition index for maintenance decision-making.

-Out-of-

The present article deals with the efficient use of different types of monitoring information in optimizing condition-based maintenance decision making for a deteriorating system operating under variable environment. The degradation phenomenon of a system is the fatigue crack growth that is modeled by a physics-based stochastic process. The environment process is assumed to be modeled by a time-homogenous Markov chain with finite state space. We suppose that the environmental condition is observed perfectly, while the crack depth can be assessed imperfectly through a non-destructive ultrasonic technique. As such, two kinds of indirect information are available on the system at each inspection time: environmental covariate and diagnostic covariate. Based on this set of information, two condition-based maintenance strategies adaptive to environmental conditions are developed. In the first one, the adaptation scheme is time-based, while in the second, it is condition-based. These maintenance strategies are compared one with another and to a classical non-adaptive one to point out the performances of each adaptation scheme and hence the appreciation of using different information sources in maintenance decision making.

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Motivated by the effectiveness of condition-based maintenance (CBM) for single-unit systems, we develop in this paper an opportunistic predictive maintenance model for a k-out-of-n deteriorating system. This model reflects the joint effects of economic dependence and CBM decision on the maintenance cost. Unlike most existing CBM models whose maintenance decision-making relies directly on a condition index of degradation level, we base the decision on the reliability of each component computed conditionally on its degradation level. This makes the model robust with respect to (w.r.t.) measurement errors, more efficient and easier to optimize compared to a corresponding degradation-based maintenance model.

:F Deteriorating Systems

Seeking condition indices characterizing the health state of a system is a key problem in condition-based maintenance. For this purpose, diagnostic and prognostic models have been unceasingly developed and improved over the past few decades; nevertheless none of them explains thoroughly the impacts of such indices on the effectiveness of maintenance operations. As a complement to these efforts, this paper analyzes the effectiveness of some well-known diagnostic and prognostic indices for maintenance decision-making. The study is based on a system subject to competing risks due to multiple crack paths. A periodic inspection scheme is used to monitor the system health state. Each inspection returns the perfect diagnostic information: the number of cracks, corresponding crack sizes, and the system failure/working state. Based on this information, two kinds of prognostic condition indices are predicted: the average value and probability law of the system residual useful life. The associated condition-based maintenance strategies and cost models are then developed and compared with the ones whose maintenance decisions are based on diagnostic condition indices. The comparison results allow us to conclude on the performance and on the robustness of these strategies, hence giving some suggestions on the choice of reliable condition indices for maintenance decision-making.

On the Use of Mean Residual Life as a Condition Index for Condition-Based Maintenance Decision-Making

Over the last few decades, many efforts have been invested in improving the economic performances of maintenance policies for stochastically deteriorating production systems. However, with the development of complex production systems, maintenance managers are interested not only in cost saving, but also in how to trustworthily plan and allocate the required maintenance budget. In this context, the robustness of maintenance policies which is related to the maintenance cost variability from a renewal cycle to another plays a pivotal role. This research deals with a quantitative approach to jointly assess the economic performance and robustness of some representatives of two most well-known classes of maintenance policies: time-based and condition-based maintenance. To this end, we first propose a new cost criterion which combines the long-run expected cost rate and standard deviation of maintenance cost per renewal cycle. Then, we develop and compare the associated mathematical cost models of the considered maintenance policies on the basis of the Gamma degradation process and the theory of stochastic renewal processes. The comparison results under different situations of maintenance costs and system characteristics show that the optimal configuration of maintenance policies gives the best compromise between the performance and robustness, and is mostly affected by the system downtime. Under this aspect, the condition-based maintenance remains more profitable than the time-based maintenance. Still, maintenance managers could implement condition-based maintenance policies that efficiently control the downtime to maximise the maintenance effectiveness of production systems from both performance and robustness viewpoints.

Adaptive condition-based maintenance decision framework for deteriorating systems operating under variable environment and uncertain condition monitoring

This paper provides a methodology to assess the efficiency of mean residual life (MRL) in condition-based maintenance decision-making for a system subjected to the competing and dependent risks of degradation and shock. Based on this system, the cost models of two quite simple policies implementing MRL-based or degradation-based preventive replacement are developed and compared with each other. Analyzing the equivalence and the performance of both policies allow us to give some conclusions on the interest of the MRL indicator.

A Reliability-based Opportunistic Predictive Maintenance Model for k-out-of-n Deteriorating Systems

In the literature, the effectiveness of a maintenance strategy is usually assessed through a cost criterion which is the long-run expected maintenance cost rate (i.e., performance viewpoint). However, such a criterion does not allow evaluating the variability of maintenance costs from a renewal cycle to another, and classical maintenance strategies seem inappropriate in the sense of risk management (i.e., robustness viewpoint). Therefore, this paper aims at (i) re-evaluating classical strategies from both performance and robustness aspects, and hence (ii) suggesting more suitable maintenance decisions. Especially, by defining the long-run expected maintenance cost rate as the performance criterion and the variance of maintenance cost per renewal cycle as the robustness criterion, we consider two representatives of time-based and condition-based maintenance families: a block replacement strategy, and a periodic inspection and replacement strategy. Their mathematical cost models are developed on the basis of the homogeneous Gamma degradation process and the theory of probability. The comparison results of both maintenance strategies show that the strategy which has a higher performance incurs a higher level of risk. So, it is necessary to assess jointly the performance and the robustness of maintenance strategies to find out a more reliable decision.

Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking

Nowadays, the health prognosis is popularly recognized as a significant lever to improve the maintenance performance of modern industrial systems. Nevertheless, how to efficiently exploit prognostic information for maintenance decision-making support is still a very open and challenging question. In this paper, we attempt at contributing to the answer by developing a new parametric predictivemaintenance decision framework considering improving health prognosis accuracy. The study is based on a single-unit deteriorating system subject to a stochastic degradation process, and to maintenance actions such as inspection and replacement. Within the new framework, the system health prognosis accuracy is used as a condition index to decide whether or not carrying out an intervention on the system. The associated mathematical cost model is also developed and optimized on the basis of the semi-regenerative theory, and is compared to a more classical benchmark framework. Numerical experiments emphasize the performance of the proposed framework, and confirm the interest of introducing the system health prognosis accuracy in maintenance decision-making.