A Novel Method for Diagnosis of Bearing Fault Using Hierarchical Multitasks Convolutional Neural Networks

Abstract

Intelligent mechanical fault diagnosis has developed very fast in recent years due to the advancement and application of deep learning technologies. Thus, there are many deep learning network models that have been explored in fault classification and diagnosis. However, there are still limitations in research on the relationship between fault location, fault type, and fault severity. In this paper, a novel method for diagnosis of bearing fault using hierarchical multitask convolution neural networks (HMCNNs) is proposed, taking into account the mentioned relationships. The HMCNN model includes a main task and multiple subtasks. In the HMCNN model, a weighted probability is used to reduce the classification error propagation among multitasks to improve the fault diagnosis accuracy. The validity of the proposed method is verified on bearing datasets. Experimental results show that the proposed method is very effective and superior to the existing methods.