Rafael Gouriveau

Learn More
Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL), i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decisionmaking processes) enable increasing availability and safety of industrial equipment while reducing costs. However,(More)
Performances of data-driven prognostics approaches are closely dependent on form, and trend of extracted features. Indeed, features that clearly reflect the machine degradation, should lead to accurate prognostics, which is the global objective of the paper. This paper contributes a new approach for features extraction / selection: the extraction is based(More)
Nonlinear 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(More)
Failure prognostics requires an efficient prediction tool to be built. This task is as difficult as, in many cases, very few knowledge or previous experiences on the degradation process are available. Following that, practitioners are used to adopt a “trial and error” approach, and to make some assumptions when developing a prediction model: choice of an(More)
Estimating 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,(More)
Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of(More)
This paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive NeuroFuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the(More)
Combining 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(More)
Various 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(More)