Fiche publication
Date publication
décembre 2015
Journal
IEEE transactions on cybernetics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr ZERHOUNI Noureddine
Tous les auteurs :
Javed K, Gouriveau R, Zerhouni N
Lien Pubmed
Résumé
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 uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.
Mots clés
Algorithms, Cluster Analysis, Engineering, Fuzzy Logic, Machine Learning, Models, Theoretical, Multivariate Analysis
Référence
IEEE Trans Cybern. 2015 Dec;45(12):2626-39
