From real data to remaining useful life estimation : an approach combining neuro-fuzzy predictions and evidential Markovian classifications.

International audienceThis paper deals with the proposition of a prognostic approach that enables to face up to the problem of lack of information and missing prior knowledge. Developments rely on the assumption that real data can be gathered from the system (online). The approach consists in three phases. An information theory-based criterion is first used to isolate the most useful observations with regards to the functioning modes of the system (feature selection step). An evolving neuro-fuzzy system is then used for online prediction of observations at any horizons (prediction step). The predicted observations are classified into the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory (classification step). The whole is illustrated on a problem concerning the prediction of an engine health. The approach appears to be very efficient since it enables to early but accurately estimate the failure instant, even with few learning data

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

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

Towards accurate and reproducible predictions for prognostic : an approach combining a RRBF Network and an AutoRegressive Model.

International audienceIn prognostic's field, the lack of knowledge on the behavior of equipments can impede the development of classical dependability analysis, or the building of effective physic-based models. Following that, artificial neural networks (ANNs) appear to be well suited since they can learn from data gathered from equipments. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and an AutoRegressive with eXogenous inputs model (ARX) is proposed in order to perform the prediction step of prognostics: the ARX attempts to correct the error of predictions of the RRBF. Moreover, since performances of an ANN can be closely related to initial parameterization of the network, a criterion is defined to quantify the reproducibility of predictions and thereby a priori estimate the usefulness of neural network structure. The whole aims at improving the prediction step of prognostics, which is critical with respects to real applicative conditions

Novel failure prognostics approach with dynamic thresholds for machine degradation.

International audienceEstimating remaining useful life (RUL) of critical machinery is a challenging task. It is achieved through essential steps of data acquisition, data pre-processing and prognostics modeling. To estimate RUL of a degrading machinery, prognostics modeling phase requires precise knowledge about failure threshold (FT) (or failure definition). Practically, degrading machinery can have different levels (states) of degradation before failure, and prognostics can be quite complicated or even impossible when there is absence of prior knowledge about actual states of degrading machinery or FT. In this paper a novel approach is proposed to improve failure prognostics. In brief, the proposed prognostics model integrates two new algorithms, namely, a Summation Wavelet Extreme Learning Machine (SWELM) and Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC) to predict degrading behavior, automatically identify the states of degrading machinery, and to dynamically assign FT. Indeed, for practical reasons there is no interest in assuming FT for RUL estimation. The effectiveness of the approach is judged by applying it to real dataset in order to estimate future breakdown of a real machinery

Defining and applying prediction performance metrics on a recurrent NARX time series model.

International audienceNonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output. Some prediction metrics are also proposed to assess the quality of predictions. This metrics enable to compare different prediction schemes and provide an objective way to measure how changes in training or prediction model (Neural network architecture) affect the quality of predictions. Results show that the proposed NARX approach consistently outperforms the prediction obtained by the RNN neural network

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

SW-ELM : A summation wavelet extreme learning machine algorithm with a priori initialization.

International audienceCombining neural networks and wavelet theory as an approximation or prediction models appears to be an effective solution in many applicative areas. However, when building such systems, one has to face parsimony problem, i.e., to look for a compromise between the complexity of the learning phase and accuracy performances. Following that, the aim of this paper is to propose a new structure of connectionist network, the Summation Wavelet Extreme Learning Machine (SW-ELM) that enables good accuracy and generalization performances, while limiting the learning time and reducing the impact of random initialization procedure. SW-ELM is based on Extreme Learning Machine (ELM) algorithm for fast batch learning, but with dual activation functions in the hidden layer nodes. This enhances dealing with non-linearity in an efficient manner. The initialization phase of wavelets (of hidden nodes) and neural network parameters (of input-hidden layer) is performed a priori, even before data are presented to the model. The whole proposition is illustrated and discussed by performing tests on three issues related to time-series application: an "input-output" approximation problem, a one-step ahead prediction problem, and a multi-steps ahead prediction problem. Performances of SW-ELM are benchmarked with ELM, Levenberg Marquardt algorithm for Single Layer Feed Forward Network (SLFN) and ELMAN network on six industrial data sets. Results show the significance of performances achieved by SW-ELM

Pronostic de défaillances : Maîtrise de l'erreur de prédiction.

International audienceLe travail rapporté ici traite globalement de la spécification et du développement d'un système de pronostic de défaillances. De ce point de vue, beaucoup de développements visant la proposition de méthodes de prévision existent dans la littérature. La majorité d'entre elles portent sur la construction de modèles capables de minimiser l'erreur de prédiction d'une situation future. Cependant, peu traitent de la maitrise de cette erreur. C'est ce qui fait l'objet de ce papier et pour lequel nous proposons d'exploiter le système ANFIS (système d'inférence floue paramétré par apprentissage neuronal). Après avoir positionné l'activité de pronostic dans le cadre de la maintenance industrielle, nous présentons le réseau ANFIS. Nous étudions les pistes permettant de maîtriser l'erreur de prédiction d'un tel système, notamment lors de la phase d'apprentissage (optimisation des paramètres du réseau). Les éléments théoriques nécessaires à cette analyse sont décrits, une nouvelle fonction de coût est proposée et l'influence de celle-ci sur les performances du réseau est discutée. Nous illustrons l'ensemble sur un benchmark. La modification proposée permet de réduire la phase d'apprentissage du système de pronostic

Pronostic industriel : étude de l'erreur de prédiction du système ANFIS.

International audienceLe travail porte globalement sur le développement d'un outil de pronostic de défaillances basé sur l'utilisation d'un système de prédiction neuro-flou. Plus particulièrement, cet article vise la proposition d'une architecture de prédiction basée sur l'utilisation du système ANFIS (système d'inférence floue paramétré par apprentissage neuronal), et pour laquelle différents axes d'améliorations des prédictions sont proposés. La stabilité des erreurs de prédictions en fonction de l'horizon de prédiction est étudiée expérimentalement et une solution visant à intégrer les sollicitations "futures" connues dans le modèle prédictif est proposée. L'ensemble est illustré sur un benchmark de prédiction : la série de données dite de Box-Jenkins