NDLI: Novel failure prognostics approach with dynamic thresholds for machine degradation

Content Provider	IEEE Xplore Digital Library
Author	Javed, K. Gouriveau, R. Zerhouni, N.
Copyright Year	2013
Description	Author affiliation: FEMTO - ST Inst., Besancon, France (Javed, K.; Gouriveau, R.; Zerhouni, N.)
Abstract	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, 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.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	4404
Ending Page	4409
File Size	1099058
Page Count	6
File Format	PDF
ISBN	9781479902248
ISSN	1553572X
DOI	10.1109/IECON.2013.6699844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-10
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Machinery Prognostics and health management State estimation Degradation Prediction algorithms
Content Type	Text
Resource Type	Article

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