Wavelet-based methods for the prognosis of mechanical and electrical failures in electric motors

被引：37

作者：

Zanardelli, WG ^{[1
]}

Strangas, EG

Khalil, HK

Miller, JM

机构：

[1] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA

[2] Ford Motor Co, Dearborn, MI 48121 USA

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2005年 / 19卷 / 02期

关键词：

fault prognosis; wavelets; d.c; motors;

D O I：

10.1016/j.ymssp.2003.10.002

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The ability to give a prognosis for failure of a system is a valuable tool and can be applied to electric motors. In this paper, three wavelet-based methods have been developed that achieve this goal. Wavelet and filter bank theory, the nearest-neighbour rule, and linear discriminant functions are reviewed. A framework for the development of a fault detection and classification algorithm based on the coefficients calculated from the discrete wavelet transform and using clustering is described. An experimental set-up based on RT-Linux is described and results from testing are presented, verifying the analysis. (C) 2003 Elsevier Ltd. All rights reserved.

引用

页码：411 / 426

页数：16

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