Genetic algorithms for feature selection in machine condition monitoring with vibration signals

Artificial neural networks (ANNs) can be used successfully to detect faults in rotating machinery, using statistical estimates of the vibration signal as input features. In any given scenario, there are many different possible features that may be used as inputs for the ANN. 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. The paper examines the use of a genetic algorithm (GA) to select the most significant input features from a large set of possible features in machine condition monitoring contexts. Using a GA, a subset of six input features is selected from a set of 66, giving a classification accuracy of 99.8%, compared with an accuracy of 87.2% using an ANN without feature selection and all 66 inputs. From a larger set of 156 different features, the GA is able to select a set of six features to give 100% recognition accuracy.