Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate

Hong Pan,Jin-Jun Xu,Wei Tang,Maxime Binama,José António Fonseca De Oliveira Correia

doi:10.1155/2020/6625273

Hong Pan, Jin-Jun Xu + Show 3 more

Open Access

https://doi.org/10.1155/2020/6625273

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Abstract

Fault diagnosis is of great significance for ensuring the safety and reliable operation of rolling bearing in industries. Stack autoencoder (SAE) networks have been widely applied in this field. However, the model parameters such as learning rate are always fixed, which have an adverse effect on the convergence speed and accuracy of fault classification. Thus, this paper proposes a dynamic learning rate adjustment approach for the stacked autoencoder network. First, the input data is normalized and enhanced. Second, the structure of the SAE network is selected. According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network. Finally, the fault diagnosis models with different learning rate adjustment are conducted in order to validate the better performance of the proposed approach. In addition, the influence of quantities of labeled sample data on the process of backpropagation is analyzed. The results show that the proposed method can effectively increase the convergence speed and improve classification accuracy. Moreover, it can reduce the labeled sample size and make the network more stable under the same classification accuracy.

Highlights

As an important part of rotating machinery, bearing plays an important role in modern industry
In order to solve the above problems, this paper proposes a novel dynamic learning rate method to replace the original fixed learning rate in pretraining and reverse fine-tuning process of bearing fault diagnosis, making the following two main contributions: (1) according to the positive and negative value of training error gradient, a learning rate reducing strategy is designed to be consistent with the current operation of the network. e convergence rate and convergence accuracy had been accelerating significantly
Network Structure. e constant pretraining learning rate was 0.1, the pretraining iteration times were 600, the reverse fine-tuning learning rate was 0.01, the labeled samples were set to 10%, the

Summary

Introduction

As an important part of rotating machinery, bearing plays an important role in modern industry. Bearing faults could cause unbearable and unpredictable loss [1, 2]. Erefore, lots of artificial intelligent (AI) fault diagnosis methods have been applied to keep the bearings working properly and reliably. The traditional AI methods are primarily based on shallow machine learning theory, which work on the original feature representation without creating new features during the learning process. With the development of condition monitoring, the data of the bearing working station become much wider than ever before, which brings new opportunities and challenges for bearing fault diagnosis. In view of the characteristics of big data such as imperfection, multisource, and low value density, the shallow machine learning method needs to be fundamentally improved.

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