A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters

International audienceWith the development of monitoring equipment, research on condition-based maintenance (CBM) is rapidly growing. CBM optimization aims to find an optimal CBM policy which minimizes the average cost of the system over a specified duration of time. This paper proposes a dynamic auto-adaptive predictive maintenance policy for single-unit systems whose gradual deterioration is governed by an increasing stochastic process. The parameters of the degradation process are assumed to be unknown and Bayes' theorem is used to update the prior information. The time interval between two successive inspections is scheduled based on the remaining useful life (RUL) of the system and is updated along with the degradation parameters. A procedure is proposed to dynamically adapt the maintenance decision variables accordingly. Finally, different possible maintenance policies are considered and compared to illustrate their performance

Passerelles entre les approches statistiques et géométriques de la détection

Les deux principaux types d'approche des problèmes de détection et d'isolation de défaillances sont rappelés ici dans le but de mettre en évidence les liens théoriques qui existent entre celles-ci. Cette tentative d'unification permet également d'envisager des problèmes de détection d'une complexité accrue et la prise en compte de certaines informations jusque là négligées

Remaining Useful Lifetime Prognosis of Controlled Systems: A Case of Stochastically Deteriorating Actuator

This paper addresses the case of automatic controlled system which deteriorates during its operation because of components' wear or deterioration. Depending on its specific closed-loop structure, the controlled system has the ability to compensate for disturbances affecting the actuators which can remain partially hidden. The deterioration modeling and the Remaining Useful Lifetime (RUL) estimation for such closed-loop dynamic system have not been addressed extensively. In this paper, we consider a controlled system with Proportional-Integral-Derivative controller. It is assumed that the actuator is subject to shocks that occur randomly in time. An integrated model is proposed to jointly describe the state of the controlled process and the actuator deterioration. Only the output of the controlled system is available to assess its health condition. By considering a Piecewise Deterministic Markov Process, the RUL of the system can be estimated by a two-step approach. In the first step referred as the "Diagnosis" step, the system state is estimated online from the available monitoring observations by using a particle filtering method. In the second step referred as the "Prognosis" step, the RUL is estimated as a conditional reliability by Monte Carlo simulation. To illustrate the approach, a simulated tank level control system is used

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

Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

The quality of condition monitoring is an important factor affecting the effectiveness of a condition-based maintenance program. It depends closely on implemented inspection and instrument technologies, and eventually on investment costs, i.e., a more accurate condition monitoring information requires a more sophisticated inspection, hence a higher cost. While numerous works in the literature have considered problems related to condition monitoring quality, (e.g., imperfect inspection models, detection and localization techniques, etc.) few of them focus on adjusting condition monitoring quality for condition-based maintenance optimization. In this paper, we investigate how such an adjustment can help to reduce the total cost of a condition-based maintenance program. The condition monitoring quality is characterized by the observation noises on the system degradation level returned by an inspection. A dynamic condition-based maintenance and inspection policy adapted to such a observation information is proposed and formulated based on Partially Observable Markov Decision Processes. The use and advantages of the proposed joint inspection and maintenance model are numerically discussed and compared to several inspection-maintenance policies through numerical examples

Case report: CAR-T cell therapy-induced cardiac tamponade

CD19-specific chimeric antigen receptor T (CAR-T) cell therapy has recently been shown to improve the prognosis of refractory diffuse large B-cell lymphoma (DLBCL). However, CAR-T cells may induce numerous adverse events, in particular cytokine release syndrome (CRS) which is frequently associated with cardiovascular manifestations. Among the latter, acute pericardial effusion represents less than 1% of cases and cardiac tamponade has only been reported once. The management and outcome of these severe complications are not well established. We report here, a case of cardiac tamponade associated with CRS in a context of CAR-T cell therapy, which required urgent pericardiocentesis.Case summaryA 65-year-old man with refractory DLBCL was treated with CAR-T cell therapy. He had a history of dilated cardiomyopathy with preserved ejection fraction and transient atrial fibrillation. A pericardial localization of the lymphoma was observed on the second relapse. One day after CAR-T cell infusion the patient was diagnosed with grade 1 CRS. Due to hypotension, he was treated with tocilizumab and dexamethasone, and then transferred to intensive care unit (ICU). Echocardiography performed at ICU admission showed acute pericardial effusion with signs of right ventricular heart failure due to cardiac tamponade. It was decided to perform pericardiocentesis despite grade IV thrombocytopenia in a context of aplasia. Analysis of pericardial fluid showed a large number of lymphoma cells and 73% of CAR-T cells amongst lymphocytes, a level that was similar in blood. Hemodynamic status improved after pericardiocentesis, and no recurrence of pericardial effusion was observed. The presence of a high count of activated CAR-T cells in the pericardial fluid as well as the short interval between CAR-T cells injection and the symptoms appear as potential arguments for a direct action of CAR-T cells in the mechanism of this adverse event. The patient was discharged from ICU after two days and initially exhibited a good response to DLBCL treatment. Unfortunately, he died fifty days after starting CAR-T cell therapy due to a new DLBCL relapse.ConclusionPatients with a pericardial localization of DLBCL should be assessed for a risk of cardiac tamponade if receiving CAR-T cell therapy and presenting CRS. In this case, cardiac tamponade seems directly related to CAR-T cell expansion. Pericardiocentesis should be considered as a feasible and effective treatment if the risk of bleeding is well controlled, in association with anti-IL6 and corticosteroids

Degradation prognosis based on a model of Gamma process mixture

International audienceA novel method is proposed to exploit jointly degradation measurements originating from a set of identical systems for making a degradation prognosis. The systems experience different degradation processes depending on operational conditions. The degradation processes are assumed to be Gamma processes. The aim is to cluster the degradation paths in classes corresponding to the different operational conditions in order to group properly the data for the estimation of degradation process parameters. A model of Gamma process mixture is considered and an expectation-minimization approach is proposed to estimate the unknown parameters. The feasibility of the method is shown on simulated cases. Prognosis results obtained with the proposed method are compared with results obtained with basic strategies (considering each system alone or all system together)

Modeling of a Degradation Path Set with a Gamma Process Mixture - Application to a Maintenance Decision Rule

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