Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms

Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical methods to preprocess the vibration signals as input features. ANNs have been shown to be highly successful in this type of application; in comparison, support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring arena. The availability of a limited amount of training data creates certain problems for the use of SVMs, and a strategy is advanced to improve the generalisation performance in cases where only limited training data is available. This paper examines the performance of both types of classifiers in two-class fault/no-fault recognition examples and the attempts to improve the overall generalisation performance of both techniques through the use of genetic algorithm based feature selection process. (C) 2002 Published by Elsevier Science Ltd.