Feature selection for ANNs using genetic algorithms in condition monitoring

Arti cial Neural Networks (ANNs) can be used successfully to detect faults in rotating machinery, using statistical estimates of the vibration signal as input features. One of the main problems facing the use of ANNs is the selection of the best inputs to the ANN, allowing the creation of compact, highly accurate networks that require comparatively little preprocessing. This paper examines the use of a Genetic Algorithm (GA) to select the most signi cant input features from a large set of possible features in machine condition monitoring contexts. Using a large set of 156 di erent features, the GA is able to select a set of 6 features that give 100% recognition accuracy.