Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization

Abstract

The failure of rotating machinery can result in fatal damage and economic loss since rotating machinery plays an important role in the modern manufacturing industry. The development of a reliable and efficient intelligent fault diagnosis approach is an ongoing attempt. Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis. But finding out good features that can discriminate different fault conditions and optimizing parameters for support vector machine can be regarded as the most two important problems that can highly affect the final diagnosis accuracy of support vector machine. Until now, the two issues of feature selection and parameter optimization are usually treated separately, weakening the effects of both efforts. Therefore, an ant colony algorithm for synchronous feature selection and parameter optimization for support vector machine in intelligent fault diagnosis of rotating machinery is presented. Comparing with other methods, the advantages of the proposed method are evaluated on an experiment of rotor system and an engineering application of locomotive roller bearings, which proves it can attain much better results.