Multi-attribute quantitative bearing fault diagnosis based on convolutional neural network

Abstract

Existing bearing fault diagnosis methods have some disadvantages, one being that most methods cannot completely consider all specific fault attributes. Another disadvantage is that the qualitative diagnosis method considers different fault types as a whole, and qualitative diagnosis of a single fault attribute is complicated. A convolutional neural network is proposed for application in the multi-attribute quantitative bearing fault diagnosis. Multiple combinations of convolutional layers are adopted to directly extract features from one-dimensional vibration signals. In addition, a softmax layer is designed to realise the simultaneous recognition of different fault attributes. The advantage of this approach is that it can realise diagnostic results for any combination of fault attributes and corresponding types, which overcomes the disadvantage of single attribute recognition in the traditional method. The method is simple but has strong generalisation ability with average diagnostic accuracy of more than 95%. According to bearing data from Case Western Reserve University and laboratory experiments by the authors, the results verify that the method can accurately and quantitatively diagnose bearing faults.