Contribution au pronostic de défaillances guidé par des données

Ce mémoire d’Habilitation à Diriger des Recherche (HDR) présente, dans la première partie, une synthèse de mes travaux d’enseignement et de recherche réalisés au sein de l’École Nationale Supérieure de Mécanique et des Microtechniques (ENSMM) et de l’Institut FEMTO-ST. Ces travaux s’inscrivent dans la thématique du PHM (Prognostics and Health Management) et concernent le développement d’une approche intégrée de pronostic de défaillances guidée par des données. L’approche proposée repose sur l’acquisition de données représentatives des dégradations de systèmes physiques, l’extraction de caractéristiques pertinentes et la construction d’indicateurs de santé, la modélisation des dégradations, l’évaluation de l’état de santé et la prédiction de durées de fonctionnement avant défaillances (RUL : Remaining Useful Life). Elle fait appel à deux familles d’outils : d’un côté des outils probabilistes/stochastiques, tels que les réseaux Bayésiens dynamiques, et de l’autre côté les modèles de régression non linéaires, notamment les machines à vecteurs de support pour la régression. La seconde partie du mémoire présente le projet de recherche autour du PHM de systèmes complexes et de MEMS (Micro-Electro-Mechanical Systems), avec une orientation vers l’approche de pronostic hybride en combinant l’approche guidée par des données et l’approche basée sur des modèles physiques.This Habilitation manuscript presents, in the first part, a synthesis of my teaching and research works achieved at the National Institute of Mechanics and Microtechnologies (ENSMM) and at FEMTO-ST Institute. These works are within the topic of Prognostics and Health Management (PHM) and concern the development of an integrated data-driven failure prognostic approach. The proposed approach relies on acquisition of data which are representative of systems degradations, extraction of relevant features and construction of health indicators, degradation modeling, health assessment and Remaining Useful Life (RUL) prediction. This approach uses two groups of tools: probabilistic/stochastic tools, such as dynamic Bayesian networks, from one hand, and nonlinear regression models such as support vector machine for regression and Gaussian process regression, from the other hand. The second part of the manuscript presents the research project related to PHM of complex systems and MEMS (Micro-Electro-Mechanical Systems), with an orientation towards a hybrid prognostic approach by considering both model-based and data-driven approaches

Residual-based failure prognostic in dynamic systems.

International audienceThis paper deals with failure prognostic in dynamic systems. The system's remaining useful life is estimated based on residual signals. This supposes the possibility to build a dynamic model of the system by using the bond graph tool, and the existence of a degradation model in order to predict its future health state. The choice of bond graph is motivated by the fact that it is well suited for modeling physical systems where several types of energies are involved. In addition, it allows to generate residuals for fault diagnostic and prognostic. The proposed method is then applied on a simple dynamic model of a hydraulic system to show its feasibility

Hybrid prognostic method applied to mechatronic systems.

International audienceFault detection and isolation, or fault diagnostic, of mechatronic systems has been subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life

Feature extraction and evaluation for Health Assessment and Failure prognostics.

International audienceThe estimation of Remaining Useful Life (RUL) of industrial equipments can be realized on their most critical components. Based on this assumption, the identified critical component must be monitored to track its health state during its operation. Then, the acquired data are processed to extract relevant features, which are used for RUL estimation. This paper presents an evaluation method for the goodness of the features, extracted from raw monitoring signals, for health assessment and prognostics of critical industrial components. The evaluation method is applied to several simulated datasets as well as features obtained from a particular application on bearings

Data-Driven prognostics based on health indicator construction : Application to PRONOSTIA's Data.

International audienceFailure prognostics can help improving the availability and reliability of industrial systems while reducing their maintenance cost. The main purpose of failure prognostics is the anticipation of the time of a failure by estimating the Remaining Useful Life (RUL). In this case, the fault is not undergone and the estimated RUL can be used to take appropriate decisions depending on the future exploitation of the industrial system. This paper presents a data-driven prognostic method based on the utilization of signal processing techniques and regression models. The method is applied on accelerated degradations of bearings performed under the experimental platform called PRONOSTIA. The purpose of the proposed method is to generate a health indicator, which will be used to calculate the RUL. Two acceleration sensors are used on PRONOSTIA platform to monitor the degradation evolution of the tested bearings. The vibration signals related to the degraded bearings are then compared to a nominal vibration signal of a nondegraded bearing (nominal bearing). The comparison between the signals is done by calculating a correlation coefficient (which is considered as the health indicator). The values of the calculated correlation coefficient are then fitted to a regression model which is used to estimate the RUL

Condition Assessment and Fault Prognostics of Microelectromechanical Systems.

International audienceMicroelectromechanical systems (MEMS) are used in different applications such as automotive, biomedical, aerospace and communication technologies. They create new functionalities and contribute to miniaturize the systems and reduce their costs. However, the reliability of MEMS is one of their major concerns. They suffer from different failure mechanisms which impact their performance, reduce their lifetime and their availability. It is then necessary to monitor their behavior and assess their health state to take appropriate decision such as control reconfiguration and maintenance. These tasks can be done by using Prognostic and Health Management (PHM) approaches. This paper addresses a condition assessment and fault prognostic method for MEMS. The paper starts with a short review about MEMS and presents some challenges identified and which need to be raised to implement PHM methods. The purpose is to highlight the intrinsic constraints of MEMS from PHM point of view. The proposed method is based on a global model combining both nominal behavior model and degradation model to assess the health state of MEMS and predict their remaining useful life. The method is applied on a microgripper, with different degradation models, to show its effectiveness

Diagnostic et pronostic de défaillances par réseaux bayésiens

Cet article a pour but de montrer l'utilisation des réseaux bayésiens statiques et dynamiques dans le domaine de la localisation des défaillances (diagnostic) et de l'anticipation ou de la prédiction des éventuelles dégradations pouvant affecter un système dynamique. Dans le premier cas, les réseaux bayésiens statiques sont utilisés pour calculer les probabilités a posteriori de ou des causes les plus probables d'une anomalie observée (observation ou évidence). Dans le second cas, les réseaux bayésiens dynamiques sont utilisés pour tenir compte de la dynamique du système et permettre de prédire son comportement futur en fonction de son état actuel et d'autres variables ou contraintes exogènes

A procedure for failure prognostic in dynamic system.

International audienceIn maintenance field, many developments exist to support the prognostic activity. However, the implementation of an adequate and efficient prognostic tool can be a non trivial task as it is difficult to provide effective models of dynamic systems including the inherent uncertainty of prognostic. In this context, the purpose of the paper is to propose a procedure to generate a prognostic model. The work is based on the integration of bond graph tool and Dynamic Bayesian Networks. The first one provides a dynamic model of the system, and the second ones, thanks to their inference capability, enable to take into account uncertainty and are well suitable to perform diagnosis and prognostic. The proposed procedure is illustrated on an hydromechanical system

Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction.

International audienceReliability of prognostics and health management systems (PHM) relies upon accurate understanding of critical components' degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of data or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HI) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline health indicator, to the online health indicator, using k-nearest neighbors (k-NN) classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications

Accelerated life tests for prognostic and health management of MEMS devices.

International audienceMicroelectromechanical systems (MEMS) offer numerous applications thanks to their miniaturization, low power consumption and tight integration with control and sense electronics. They are used in automotive, biomedical, aerospace and communication technologies to achieve different functions in sensing, actuating and controlling. However, these Microsystems are subject to degradations and failure mechanisms which occur during their operation and impact their performances and consequently the performances of the systems in which they are used. These failures are due to different influence factors such as temperature, humidity, etc. The reliability of MEMS is then considered as a major obstacle for their development. In this context, it is necessary to continuously monitor them to assess their health status, detect abrupt faults, diagnose the causes of the faults, anticipate incipient degradations which may lead to complete failures and take appropriate decisions to avoid abnormal situations or negative outcomes. These tasks can be performed within Prognostics and Health Management (PHM) framework. This paper presents a hybrid PHM method based on physical and data-driven models and applied to a microgripper. The MEMS is first modeled in a form of differential equations. In parallel, accelerated life tests are performed to derive its degradation model from the acquired data. The nominal behavior and the degradation models are then combined and used to monitor the microgripper, assess its health state and estimate its Remaining Useful Life (RUL)