Author:
Che Changchang,Wang Huawei,Ni Xiaomei,Fu Qiang
Abstract
Purpose
The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.
Design/methodology/approach
The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.
Findings
The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.
Originality/value
The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/
Subject
Surfaces, Coatings and Films,General Energy,Mechanical Engineering
Cited by
14 articles.
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