Condition-based monitoring (CBM) is widely used methodology for the fault diagnosis, which provides the analysis for the safe and proper operations of any device or element. Vibration analysis is most accurate and reliable technique of CBM to reveal the condition of device or element. In this technique, fault can be diagnosed by analysing the vibration data acquired from accelerometer. Convolutional Neural Network (CNN) has emerged as one of the most widely used methodology in application of pattern recognition and acoustic data analysis. In this paper, CNN is used as back-end classifier for bearing fault detection. Vibration data is collected for three different conditions of bearings i.e. normal condition, inner race fault and outer race fault. Statistical features are extracted from vibration data and used as input to CNN classifier. Convolution filters are learned by training CNN and are used to detect the unique features for each condition of bearing. The obtained accuracy shows that CNN is very reliable and effective technique for bearing fault diagnosis. It exhibits good performance compared to peer algorithms. © 2017 IEEE.
cited By 0; Conference of 2017 IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017 ; Conference Date: 20 April 2017 Through 21 April 2017; Conference Code:131156
M. Bhadane and Ramachandran, K. I., “Bearing fault identification and classification with convolutional neural network”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017, 2017.