Automated feature extraction using genetic programming for bearing condition monitoring

The feature extraction is one of the major challenges for the pattern recognition. This helps to maximise the useful information from the raw data in order to make the classification effective and simple. In this paper, one of the machine learning approaches, genetic programming (GP), is employed to extract features from the raw vibration data taken from a rotating machine with several different conditions. The created features are then used as the input to a simple ANN for the identification of different bearing conditions, in comparison with the other classical machine learning methods. Experimental results demonstrate the capability of GP to discover automatically the functional relationships among the raw vibration data, to give improved performance