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

Yong-Zhi Liu,Guo-Fu Ding,Yi-Sheng Zou,Yu-Liang Jiang,Hui Yu,M Z Naser

doi:10.1155/2020/8846822

Yong-Zhi Liu, Guo-Fu Ding + Show 4 more

Open Access

https://doi.org/10.1155/2020/8846822

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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.

Highlights

Rolling bearings, as the key parts of mechanical equipment, are widely used in rail transit equipment, construction machinery, precision machine tools, instrumentation, and other fields
Diagnosis Results of hierarchical multitask convolution neural networks (HMCNNs). e experiments are divided into three parts. e first part is a comparison among the proposed model, traditional method, and intelligent algorithm based on deep learning to demonstrate the superiority of the proposed model in terms of generalization performance. e second part is to extend and compare the
We study and analyze the relationship between the hierarchical tasks’ number of HMCNN model and its diagnosis accuracy

Summary

Introduction

As the key parts of mechanical equipment, are widely used in rail transit equipment, construction machinery, precision machine tools, instrumentation, and other fields. At present, bearing fault diagnosis is usually based on data-driven methods. Data-driven fault diagnosis generally includes two steps: fault feature extraction and fault classification. Convolutional neural network (CNN) is a kind of feed-forward multistage neural network. It mainly contains three kinds of layers: convolutional layer, pooling layer, and fully connected layer. E convolution layer is designed to extract different features of input data. E pooling layer following the convolutional layer is to reduce the parameters of the network through extracting the local mean or maximum value of input data. A fully connected layer is usually built in the last part of the hidden layer of the convolutional neural network. It is used to extract features and classify vibration signals in bearing fault diagnosis

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Results

Conclusion