Diagnosis of roller bearing defects using neural networks

A new approach for the diagnosis of bearing defects has been utilised. Artificial neural networks (ANN) were employed for the diagnosis of various kinds of bearing defects. The features selected for this purpose were: the average of the top five values of amplitude in the high-frequency region (5 kHz-22 kHz), the peak value of the amplitude in the high-frequency region, the average of the top five values in the prime spike region (340 Hz-3262 Hz), the autocorrelation function in the prime spike region, the autocorrelation function in the high-frequency region, and the cepstrum function in the high-frequency region.Data were collected using a data acquisition system. The data collected for the five different defective roller bearings as well as for a normal bearing were used to train neural networks. The trained neural networks were used for the diagnosis of roller bearings. Various neural network sizes were used. It was found that neural networks were able to distinguish a normal bearing from defective bearings with 100% reliability. Furthermore, roller bearings can be classified into six different states with a success rate of up to 94%.