An exTS based Neuro-Fuzzy algorithm for prognostics and tool condition monitoring.

International audienceThe growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving eXtended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed machining conditions. The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Multiple Regression Model (MRM) in term of accuracy and reliability through a case study of tool condition monitoring. The reliability of exTS also investigated

Remaining Useful Life Estimation by Classification of Predictions Based on a Neuro-Fuzzy System and Theory of Belief Functions.

International audienceVarious approaches for prognostics have been developed, and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets to build a model of the degradation signal, and estimate the limit under which the degradation signal should stay. Applicability and accuracy of these methods are thereby closely related to the amount of available data, and even sometimes requires the user to make assumptions on the dynamics of health states evolution. Following that, the aim of this paper is to propose a method for prognostics and remaining useful life estimation that starts from scratch, without any prior knowledge. Assuming that remaining useful life can be seen as the time between the current time and the instant where the degradation is above an acceptable limit, the proposition is based on a classification of prediction strategy (CPS) that relies on two factors. First, it relies on the use of an evolving real-time neuro-fuzzy system that forecasts observations in time. Secondly, it relies on the use of an evidential Markovian classifier based on Dempster-Shafer theory that enables classifying observations into the possible functioning modes. This approach has the advantage to cope with a lack of data using an evolving system, and theory of belief functions. Also, one of the main assets is the possibility to train the prognostic system without setting any threshold. The whole proposition is illustrated and assessed by using the CMAPPS turbofan dataset. RUL estimates are shown to be very close to actual values, and the approach appears to accurately estimate the failure instants, even with few learning data

A neuro-fuzzy self built system for prognostics : a way to ensure good prediction accuracy by balancing complexity and generalization.

International audienceIn maintenance field, prognostics is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. However, it can be a non trivial task to develop and implement effective prognostics models including the inherent uncertainty of prognostics. Moreover, there is no systematic way to construct a prognostics tool since the user can make some assumptions: choice of a structure, initialization of parameters... This last problem is addressed in the paper: how to build a prognostics system with no human intervention, neither a priori knowledge? The proposition is based on the use of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based on the Akaike information criterion. It consists in using a cost function in the learning phase in order to automatically generate an accurate prediction system that reaches a compromise between complexity and generalization capability. The proposition is illustrated and discussed

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions.

International audienceCondition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations

Long term prediction approaches based on connexionist systems - A study for prognostics application.

International audienceData-driven approaches are increasingly applied to machine prognostics. More precisely, connexionist systems like neural networks and neuro-fuzzy systems benefit from a growing interest. Indeed, their approximation capability makes them as powerful candidates to achieve the prediction step of prognostics. Nevertheless, prognostic implies to be able to perform multistep ahead predictions whereas many works focus on short term predictions. Following that, the aim of this paper is to review and discuss the connexionist-systems-based approaches to ensure long term predictions for prognostics. The paper emphasizes on univariate time series forecasting. Five connexionist-systemsbased approaches are pointed and formalized, namely: the iterative, direct, DirRec, parallel and MISMO approaches. Their performances are analyzed according to three types of criteria: those one of prediction accuracy, of complexity (computational time) and of implementation requirements. In addition, simulations are made among 111 times series prediction problems in order to reinforce the discussion. These experiments are performed by using the exTS (evolving extended Takagi-Sugeno system). Finally developments are applied on a real engine fault prognostics problem in order to validate conclusions on a real world case and to point out some best practices for prognostics applications

Development of a prognostic tool to perform reliability analysis.

International audienceIn maintenance field, prognostic is recognized as a key feature as the estimation of the remaining useful life of an equipment allows avoiding inopportune maintenance spending. However, it can be difficult to define an implement an adequate and efficient prognostic tool that includes the inherent uncertainty of the prognostic process. Within this frame, neuro-fuzzy systems are well suited for practical problems where it is easier to gather data (online) than to formalize the behavior of the system being studied. In this context, and according to real implementation restrictions, the paper deals with the definition of an evolutionary fuzzy prognostic system for which any assumption on its structure is necessary. The proposed approach outperform classical models and is well fitted to perform a priori reliability analysis and thereby optimize maintenance policies. An illustration of its performances is given by making a comparative study with an other neuro-fuzzy system that emerges from literature

Error estimation of a neuro-fuzzy predictor for prognostic purpose.

International audiencePrognostic is recognized as a key feature as the estimation of the remaining useful life of an equipment allows avoiding inopportune maintenance spending. However, it can be difficult to implement an efficient prognostic tool since the lack of knowledge on the behavior of an equipment can impede the development of classical dependability analysis. In this context, the general purpose of the work is to define a prognostic system for which any assumption on its structure is necessary: it starts from monitoring data and goes through provisional reliability and remaining useful life by characterizing the uncertainty following from the degradation process. Developments are founded on the use of the evolving eXtended Tagaki-Sugeno system as a neurofuzzy predictor. A method to estimate the probability distribution function of the predicted degradation signal is proposed. It enables to perform a priori reliability analysis. The approach is based on a recursive calculation procedure and is thereby well adapted to online applications

From monitoring data to remaining useful life : an evolving approach including uncertainty.

International audienceAlthough prognostic activity is nowadays recognized as a key feature in maintenance strategies, real prognostic systems are scarce in industry. That can be explained from different aspects, one of them being the lack of knowledge on the monitored system that impedes the development of classical dependability analysis (based on statistical data for example). Within this frame, the general purpose of the work is to propose a prognostic system that starts from monitoring data and goes through provisional reliability and remaining useful life by characterizing the uncertainty following from the degradation process. More precisely, the paper emphasizes on the development of an evolving neuro-fuzzy predictor that, not only "gives" an approximation of the degradation of an equipment but also associates to it a confidence measure

Defining and implementing a distributed and reconfigurable information system for prognostics.

International audienceAccording to Condition Based Maintenance practitioners, various activities, ranging from data collection through the recommendation of specific maintenance actions, must be carried out to perform predictive maintenance. Nevertheless, in practice, (and in spite of recommendations like those ones of the OSA-CBM standard), defining and implementing a computer software system for CBM is not a trivial task. That can be mostly explained by the necessity of providing a distributed application that enables to share data and information in an easy but effective manner in-between various actors from various industrial plants. Following that, the aim of the paper is to describe a collaborative software that has been developed in the society e-m@systec. Its simple architecture, as well as its evolving and customizable capabilities make the global information system as useful for distributed applications. The usage of JEE technology improves the portability of the system. This software is well adapted to support predictive maintenance strategies. Thereby and as for an illustration, an example related to a prognostic problem is also described

Contribution à l’optimisation des processus de prédiction et de classification pour le Prognostics and Health Management.

My postdoctoral research deals with the proposition of data-based failure detection and prognostics approaches. Developments aim at transforming raw data (gathered online on the monitored system) into health indicators that enable estimating the Remaining Useful Life (RUL) and the confidence interval associated to it. Three sets of contributions can be distinguished.–Characterize degradation phenomena. I mainly proposed a new features extraction and selection approach that allows building monotonic and predictable features. This proposition enhances detection of drifts and improve long term predictions of the ageing behavior.–Improve genericity of prognostics. From this point of view, my intent was to develop neural networks and neuro-fuzzy systems that aim at facilitating the building of prognostics algorithms by systematizing the generation of models while reducing the learning time required and avoiding arbitrary choices.–Reliable data-based prognostics. I developed news neuronal algorithms that enable, on one side, to quantify and master the error of prediction, and on the other side, to take into account the non-deterministic aspect of health states.The research project is articulated as follows (short term and mid-term perspectives). First, I would like to develop protocols and metrics for verification and validation of PHM approaches. I am also willing to go through a better modeling of multidimensional systems (physics, time, space). Finally, my intent is to extend PHM to what could be called the “predictive medicine”.L’essentiel des travaux de recherche post-doctorale porte sur le développement d’approches orientées données de détection et de pronostic de défaillances. Les propositions faites visent à transformer un ensemble de données brutes recueillies sur l’équipement surveillé, en un indicateur de temps résiduel avant défaillance (RUL) auquel est associée une confiance. Trois foyers de contributions peuvent être distingués.–Caractériser les phénomènes de dégradation. Je me suis attaché à proposer une nouvelle approche d’extraction et de sélection de descripteurs permettant in fine d’obtenir des indicateurs de santé monotones facilitant la détection d’une dérive et les prédictions à long terme.–Améliorer la généricité du pronostic. De ce point de vue, mon travail a consisté à proposer des méthodes (neuronales et neuro-floues) permettant de systématiser la génération des modèles de pronostic, et de réduire le temps d’apprentissage nécessaire.–Fiabiliser le pronostic orienté données. J’ai sur cet aspect développé de nouveaux algorithmes permettant d’une part, de quantifier et maîtriser l’erreur de prédiction, et d’autre part, de tenir compte de la nature non−déterministe des états de santé.Le projet de recherche à court et moyen terme s’articule autour de trois axes. Le premier a trait à la définition de protocoles et de métriques de vérification et de validation des approches de PHM. Le second porte sur le développement d’outils pour les systèmes multidimentsionnels (physique, temps, espace). Le dernier vise à étendre la thématique à un cadre applicatif pour lequel elle n’a pas été initialement imaginée : le PHM médical