Politiques de Tests Partiels & Systèmes de Sécurité

International audienceA set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to partial and full tests. Partial tests (e.g. visual inspections, imperfect testing) may detect only some failures, whereas owing to a full test, the system is restored to an as good as new condition. Following the proposed approach and according to an example, performance estimations of the system and test policies are presented, by using the feedback from partial and full tests. An optimization of the partial test distribution is also proposed, which allows reducing the average probability of system failure on demand (PFDavg)

Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Optimisation de la politique de maintenance pour un système à dégradation graduelle stressé

International audienceThis paper investigates a maintenance policy allowing the maintenance cost optimization per unit of time combining statistical process control (SPC) and condition-based maintenance (CBM) policy. We consider a single-unit system with two failure modes which can be partially explained by several covariates. Failure modes are a continuous-state deterioration and a stress. A CBM policy is used for inspecting and replacing the system in order to balance the impacts of an excessive deterioration level whereas a control a classical control chart is used to monitor the stress covariate. Sensitivity analysis based on numerical results is proposed

Method for computing efficient electrical indicators for offshore wind turbine monitoring

International audienceOffshore wind turbines availability is an important issue if such wind farms are to be considered a reliable source of renewable energy for the future. Environmental conditions and the low accessibility of such wind farms have contributed to the decrease of the availability of the wind turbines, compared to the onshore ones. In order to improve the reliability, condition monitoring systems and the implementation of scheduled maintenance strategies are a must for offshore power plants. This paper proposes a method of computing efficient electrical indicators using the available three-phase electrical quantities. These indicators are then to be used to obtain fault indicators for fault detection and diagnosis. The electrical indicators are obtained by using the instantaneous symmetrical components decomposition, a well proven method in power networks design and diagnosis. The new quantities are able to fully describe the whole electrical system and provide an effective mean to quantify the balance and unbalance in the system. The method uses the electrical three-phase quantities measured at the output of the generator in a wind turbine to obtain the indicators. The performance of this method is illustrated using both synthetic and experimental data

A Perturbed Inverse Gaussian Process Model with Time Varying Variance-To-Mean Ratio

International audienceThe inverse gaussian (IG) process has become a common model for reliability analysis of monotonic degradation processes. The traditional IG process model assumes that the degradation increment follows an IG distribution, and the variance-to-mean ratio (VMR) is constant with time. However, for the degradation paths of some practical applications, e.g., the GaAs laser degradation data that motivated to propose the IG process, the VMR is actually time varying. Confronted with this, we propose an IG process model with measurement errors that depend on the actual degradation level. According to different forms or parameter values of the dependence function, the VMR of the degradation paths can display different time varying patterns. The maximum likelihood estimation method is developed in a step-by-step way, combined with numerical integration method and heuristic optimization method. Finally, the GaAs laser example is revisited to illustrate the effectiveness of the proposed model, which indicates that the introduction of statistically dependent measurement error can provide better fitting results and lifetime evaluation performance

Hidden Markov Models for diagnostics and prognostics of systems under multiple deterioration modes

International audienceMulti-state systems have recently attracted a great deal of interest with regards to reliability and maintenance. Since most mechanical equipment operates under some sorts of stress or load, it tends to degrade over time, thus possibly resulting in discrete degradation states (damage degrees), ranging from perfect functioning to complete failure. Over recent years, Hidden Markov Models (HMMs) have been applied to model these discrete degradation states for diagnostic and prognostic purposes. However, most of the reported researches on HMMs for multi-state equipment in the literature consider only one degradation mechanism of degradation processes. The present paper proposes a novel model called multi-branch HMM (MB-HMM) to deal with deterioration processes modeling under multiple competing modes. To illustrate the proposed approach, a numerical study is given

Predictive maintenance policy for a gradually deteriorating system subject to stress

International audienceThis 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

Probability of Failure of Safety-Critical Systems Subject to Partial Tests

A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to proof tests. The proof tests can be partial or full. The partial tests (e.g. visual inspections, partial stroke testing) are able to detect only some system failures and leave the others latent, whereas the full tests refer to overhauls which restore the system to an as good as new condition. Partial tests may occur at different time instants (periodic or not), up to the full test. The system performances which are investigated are the system availability according to time, the PFD average in each partial test time interval, and the total PFD average calculated on the full test time interval. Following the given expressions, parameter estimations are proposed to assess the system failure rates and the partial test effectiveness according to feedback data from previous test policies. Subsequently, an optimization of the partial test strategy is presented. In the 2oo6 system given as example, an improvement of about 10% of the total PFD average has been obtained, just by a better (non-periodic) distribution of the same number of partial tests, in the full test time interval

Fiabilité des Capteurs-Transmetteurs intégrant des Fonctionnalités Numériques

These works are part of a PhD thesis performed at the INERIS, under the scientific supervision of the UTT, and about the reliability of digital-based transmitters. These systems are commonly described as “intelligent” since they are able to perform innovative functionalities such as self-diagnoses, error measurement corrections, self-adjustments, and on-line reconfigurations. Moreover, they may take advantage of a bidirectional digital communication to perform “cooperating” operations. First, an “intelligent transmitter” modelling has been developed, which includes material and functional interactions, and reliability analyses have been proposed based on this model, dealing with uncertainties linked to system behaviours under faulty conditions. Then, “intelligent transmitters” taking part of control systems have been considered, taking the system elements interactions into account, as well as the process influences, using a dynamic reliability framework

Multi-Branch Hidden Semi-Markov Modeling for RUL Prognosis

International audienceDeterioration modeling and remaining useful life (RUL) estimation of equipment are key enabling tasks for the implementation of a predictive maintenance (PM) policy, which plays nowadays an important role for maintaining engineering systems. Hidden Markov Models (HMM) have been used as an efficient tool for modeling the deterioration mechanisms as well as for estimating the RUL of monitored equipment. However, due to some assumptions not always justified in practice, the applications of HMM on real-life problems are still very limited. To tackle this issue and to relax some of these unrealistic assumptions, this paper proposes a multi-branch Hidden semi-Markov modeling (MB-HSMM) framework. The proposed deterioration model comprises several different branches, each one being itself an HSMM. The proposed model offers thus the capacity to 1) explicitly model the sojourn time in the different states and 2) take into account multiple co-existing and competing deterioration modes, even within a single component. A diagnosis and RUL prognosis methodology based on the MB-HSMM model is also proposed. Thanks to its multiple branches property, the MB-HSMM model makes it possible not only to assess the current health status of the component but also to detect the actual deterioration mechanism. Based on the diagnostic results, the component RUL can then be calculated. The performance of the proposed model and prognosis method is evaluated through a numerical study. A Fatigue Crack Growth (FCG) model based on the Paris-Erdogan law is used to simulate deterioration data of a bearing under different operation conditions. The results show that the proposed MB-HSMM gives a very promising performance in deterioration mode detection as well as in the RUL estimation, especially in the case where these deterioration modes exhibit very different dynamics