期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：55
期号：3
出版社：Journal of Theoretical and Applied
摘要：The This paper deals with a data-driven diagnostic and prognostic method based on Stable distribution feature extraction and SVM Classier. The prognostic process of the proposed method is made in two steps. In the first step, which is performed online, the monitoring data provided by sensors are processed to extract features based on stable distribution, which are then used to learn SVM classifier that capture the time evolution of the degradation and therefore of the systems health state. In the second step, performed on-line, the learned models are exploited to do failure prognostic by estimating the assets current health state, its remaining useful life. The experiments on the recently published database taken from Pronostia of FEMTO, Prognostic data repository: Bearing data set, clearly show the superiority of the proposed approach compared to well establish method in literature.
