Estelle Deloux

Predictive maintenance policy for a gradually deteriorating system subject to stress

This paper deals with a predictive maintenance policy for a continuously deteriorating system subject to stress. We consider a system with two failure mechanisms which are, respectively, due to an excessive deterioration level and a shock. To optimize the maintenance policy of the system, an approach combining statistical process control (SPC) and condition-based maintenance (CBM) is proposed. CBM policy is used to inspect and replace the system according to the observed deterioration level. SPC is used to monitor the stress covariate. In order to assess the performance of the proposed maintenance policy and to minimize the long-run expected maintenance cost per unit of time, a mathematical model for the maintained system cost is derived. Analysis based on numerical results are conducted to highlight the properties of the proposed maintenance policy in respect to the different maintenance parameters.

A condition-based maintenance policy for multi-component systems with Lévy copulas dependence

Abstract In this paper, we propose a new condition-based maintenance policy for multi-component systems taking into account stochastic and economic dependences. The stochastic dependence between components due to common environment is modelled by Levy copulas. Its influence on the maintenance optimization is investigated with different dependence degrees. On the issue of economic dependence providing opportunities to group maintenance activities, a new maintenance decision rule is proposed which permits maintenance grouping. In order to evaluate the performance of the proposed maintenance policy, we compare it to the classical maintenance policies.

Maintenance policy for a deteriorating system evolving in a stressful environment

This paper deals with the maintenance optimization of a system subject to a stressful environment. The system deterioration behaviour can be modified by the environment; the degradation mode can change due to the random evolution of the stressful environment. Reciprocally, the environment conditions can be influenced by the system state and as a consequence, a change in the environment can be an indicator of the system state. This paper describes a condition-based maintenance decision framework to tackle the potential variations in the system deterioration, and especially in the deterioration rate, and the new information on the system state given by the evolution of the environmental variables.

On the Use of Time-Limited Information for Maintenance Decision Support: A Predictive Approach under Maintenance Constraints

This paper deals with a gradually deteriorating system operating under an uncertain environment whose state is only known on a finite rolling horizon. As such, the system is subject to constraints. Maintenance actions can only be planned at imposed times called maintenance opportunities that are available on a limited visibility horizon. This system can, for example, be a commercial vehicle with a monitored critical component that can be maintained only in some specific workshops. Based on the considered system, we aim to use the monitoring data and the time-limited information for maintenance decision support in order to reduce its costs. We propose two predictive maintenance policies based, respectively, on cost and reliability criteria. Classical age-based and condition-based policies are considered as benchmarks. The performance assessment shows the value of the different types of information and the best way to use them in maintenance decision making.

Environmental information adaptive condition-based maintenance policies

This article deals with the construction and optimisation of accurate condition-based maintenance policies for cumulative deteriorating systems. In this context, the system condition behaviour can be influenced by different environmental factors which contribute to an increase or decrease in the degradation rate. The observed condition can deviate from the expected condition if the degradation model does not embrace these environmental factors. Moreover, if more information is available on the environment variations, the maintenance decision framework should take advantage of this new information and update the decision. The question is how shall we model the decision framework for this? We propose to model the effect of the random operating environment on the system behaviour with a randomisation of the Gamma process-degradation parameters. A new decision rule is introduced to update the maintenance decision in case of a significant deviation from the expected condition. The performance of the introduction of this new decision rule is discussed according to different contexts: the level of knowledge on thereal stress data and the restriction of a potential updating in the policy. A mathematical framework and optimisation procedures are presented and numerical experiments are conducted to highlight the benefits of the different models.

Generalization of the proportional hazards model for maintenance modelling and optimization

A non-observable monotonically deteriorating system is considered. The observed data are the maintenance times and the kinds of maintenance performed. The aim of this paper is to propose a model of the dependency between all kinds of maintenance by considering imperfect maintenance efficiency. Furthermore, we present an additional planned preventive maintenance policy that reduces a finite horizon average total maintenance cost. To deal with such a problem a competing risks model is considered. A general class of models, denoted generalized proportional hazards, is presented and a particular model is derived. An estimation method is detailed for the proposed model. Finally, by numerical implementation, a maintenance policy that leads to the lowest finite horizon average total maintenance cost is proposed.

Maintenance policy for a non-stationary deteriorating system

This paper deals with the maintenance optimization of a system subject to a stressful environment. The behavior of system deterioration can be modified by the environment. Reciprocally, the environment condition can be influenced by the system state and so, a change in the environment can be an indicator of the system state. We propose a condition-based maintenance decision framework to tackle the potential variations in the system deterioration, and especially in the deterioration rate, and the new information on the system state given by the evolution of the environmental variable. In this work, a degradation model is first developed to integrate the reciprocal influence on the system behaviour and the environment. A specific maintenance policy is constructed which combines a classical condition-based maintenance policy for the system state with a condition monitoring method to track the environmental changes. A long-run maintenance cost criteria is developed and numerical experiments are provided to highlight the benefits of our approach. The main conclusion of this study is the necessity to take into account the environment influence on the system state and we provide here an adequate maintenance framework for the decision-maker.

Health-and-usage-based maintenance policies for a partially observable deteriorating system

The aim of this paper is to discuss the modelling and optimisation of condition-based maintenance policies for systems submitted to usage profile changes. The considered system undergoes a monotone deterioration (gamma process) and is impacted by the usage conditions (covariates) via the proportional hazards model. Four different policies are proposed and the optimal maintenance parameters minimising the long-run average maintenance cost are derived. The different maintenance policies are numerically compared using Monte Carlo simulations.

On the use of time-limited and monitoring information for maintenance decision support

Abstract Developments of monitoring techniques provide information on the actual conditions. The information level available on the system (failure times, deterioration, load, usage condition, etc.) and the way to integrate them impact directly the performance of maintenance operations that represent a substantial portion of the total life cycle costs of many systems. In this context, we consider a gradually deteriorating system operating under an uncertain environment. The information about the future environment state is only known on a finite rolling horizon. The system is subject to constraints and maintenance actions cannot be planned at any time, but at fixed times called maintenance opportunities and known only on a finite horizon. Based on the considered system, we aim to use the monitoring data and the time-limited information for maintenance decision support in order to reduce its costs. Several maintenance policies are proposed: age-based policy, condition-based policy and two predictive policies based on a cost and a risk criterions. The comparison of the maintenance cost savings of these policies allows concluding the value of different types of information and the best ways to use them in maintenance decision-making.

Maintenance scheduling for multi-unit systems with hierarchical dependences

In this paper, we consider the issue of multi-component systems with hierarchical dependences which is unexplored in literature. Complex systems often can be divided into several subsystems where dependences between components are non-symmetrical due to stronger dependences within subsystems than those between subsystems. The nested Levy copulas can be used in such a case. In addition, the economic dependence which provides grouping opportunities to reduce the maintenance cost is also considered. Five condition-based maintenance policies are proposed and evaluated to minimize the expected long-run maintenance cost.