Détection statistique optimale dans un système linéaire en présence de paramètres de nuisance

- Le but de cette communication est de proposer un outil statistique optimal pour détecter un «signal» dans un système linéaire stochastique (dynamique) en présence des incertitudes (paramètres de nuisance). On suppose que les paramètres de nuisance sont inconnus et non aléatoires, ce qui signifie qu'en pratique ces derniers peuvent être choisis de façon à ce qu'ils maximisent leur négatif impact sur le système (par exemple, en dissimulant le signal). On illustre les méthodes proposées à l'aide d'un exemple du contrôle de l'intégrité en navigation par satellites

Safety Instrumented System reliability evaluation with Influencing Factors

International audienceThe relevance of reliability evaluation strongly depends on the quality of input data as failure rates. Reliability data handbooks give generic values which do not often fit system specificities. This paper deals with influencing factors in order to take into account some aspects as design, environment and use in reliability evaluations. Once a definition and a classification are proposed, a brief review of existing models is presented. This paper also introduces a new failure rate evaluation with influencing factors especially developed for safety instrumented systems. The seven-step methodology combines both qualitative and quantitative analyses to compensate for a potential lack of feedback knowledge. Some criteria are used to set a failure rate within a prior interval, according to system conditions. An application regarding safety pressure relief valves is included. The expected better argued and accurate results aim at leading to a more efficient risk management

New methodology for improving the inspection policies for degradation model selection according to prognostic measures

Health monitoring data are vital for failure prognostic and maintenance planning. Continuous monitoring data or frequent inspections can provide a large amount of information on degradation evolution and therefore ensure the quality of deterioration modeling and the lifetime prognostic accuracy. However, they are usually very costly, and sometimes inpractible in real engineering applications. Therefore, it is essential to address the issue of the appropriate amount of monitoring data. This paper proposes a new methodology to help the companies improving their actual inspection/monitoring policy to reduce operation and maintenance costs but also ensure the information quality. We investigate different types of inspection policies including periodic or non-periodic ones by considering multiples functions of the system degradation state that are linear, concave or convex. The best policies are chosen based on a multiobjective optimization problem dealing with the inspection cost and the information level. The advantages and disadvantages of the proposed methodology are discussed through numerous numerical examples for different types of degradation process, particularly Wiener and Gamma processes that have been largely addressed in the framework of degradation modeling

Model selection for degradation modeling and prognosis with health monitoring data

Health monitoring data are increasingly collected and widely used for reliability assessment and lifetime pre- diction. They not only provide information about degradation state but also could trace failure mechanisms of assets. The selection of a deterioration model that optimally fits in with health monitoring data is an important issue. It can enable a more precise asset health prognostic and help reducing operation and maintenance costs. Therefore, this paper aims to address the problem of degradation model selection including goals, procedure and evaluation criteria. Focusing on continuous degradation modeling including some currently used Lévy processes, the performance of classical and prognostic criteria are discussed through numerous numerical examples. We also investigate in what circumstances which methods perform better than others. The efficiency of a new hybrid criterion is highlighted that allows to take into account the information of goodness-of-fit of observation data when evaluating prognostic measure

Task-driven time-dependent maintenance optimization using statistical learning

International audienceIn this work, a multi-component series system is considered. The failure is not self-announced and the systemis periodically inspected. At inspection times, the failed components are repaired and the type of repairdepends on the failure time. The general maintenance actions are perfect corrective maintenance, imperfectcorrective maintenance and minimal corrective maintenance actions for the failed component, which can beconsidered as imperfect corrective maintenance for the whole system. The inspection interval is consideredas a decision parameter, and the maintenance policy is optimized using the long-run cost rate function basedon the renewal reward theorem.It is assumed that there is historical data storage for the system that includes information related to pastrepairs. It is considered that there is no information related to components’ lifetime distributions and theirparameters. The optimal decision parameter is derived considering historical data using density estimationand statistical learning algorithms like the random forest, KNN and Naïve Bayes. Eventually, the efficiencyof the proposed optimal decision parameter according to available data is compared to the one derived whereall information on the system is available

On-line change detection with nuisance parameters

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On-line change detection and condition-based maintenance for systems with unknown deterioration parameters

International audienceThe aim of this paper was to propose a condition-based maintenance policy for a gradually deteriorating system subject to change in the deterioration rate using on-line detection algorithms. The parameters of the deterioration rate after the change is unknown. The main purpose is to estimate these unknown parameters in order to adapt the condition-based maintenance policy and above all to optimize a global cost criterion

Condition-based maintenance and monitoring for stochastic gradually deteriorating systems

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On-line change detection and condition-based maintenance for a gradually deteriorating system (ICCAS 2012)

International audienceThe aim of this paper is to propose an efficient condition-based maintenance policy for a gradually deteriorating system. In this purpose a stochastic deterioration model is proposed according to the feedback data and once the deterioration is modelled a condition-based maintenance policy is proposed which minimises the long run mean average cost. The performances of the proposed condition-based maintenance policy are evaluated through Monte-Carlo simulation methods. To illustrate results Stress Corrosion Cracking phenomenon is considered

Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition

International audienceThe aim of this paper is to propose an adaptive maintenance model for a gradually deteriorating system. The system considered initially deteriorates with a nominal deterioration rate and at an unknown time the system's deterioration rate changes and the new deterioration rate is a time-dependent function. To deal with the transition of mode of deterioration in the framework of the maintenance decision rule an adequate online change detection algorithm is used. The maintenance decision rule is chosen in order to minimise the total maintenance cost including the cost of unavailability. The main result of this paper is to point out the interest of using a detection algorithm and hence the appreciation of a decision rule which takes into account transitions in the deterioration rate