Abstract
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.
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