Fault detection using genetic programming

被引：85

作者：

Zhang, L ^{[1
]}

Jack, LB ^{[1
]}

Nandi, AK ^{[1
]}

机构：

[1] Univ Liverpool, Dept Elect Engn & Elect, Signal Proc & Commun Div, Liverpool L69 3GJ, Merseyside, England

来源：

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

基金：

英国生物技术与生命科学研究理事会;

关键词：

genetic programming; feature selection; condition monitoring; fault detection; roller bearing;

D O I：

10.1016/j.ymssp.2004.03.002

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). In this paper, GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：271 / 289

页数：19

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