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. 2022 Sep 27:2022:8913859.
doi: 10.1155/2022/8913859. eCollection 2022.

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Bo Zhang  1
Affiliations

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Bo Zhang. Comput Intell Neurosci. .

Abstract

The current situation of frequent small-scale accidents shows that the existing methods have not completely solved the problem of bearing failures, and new research methods need to be used to complete the study of bearing failures. To prevent the failure of rolling bearings and meet the need for timely detection of faults, this research is based on deep learning. Using the combination of deep transfer learning and metric learning methods, the identification and analysis of bearing multi-state vibration signals under different working conditions are carried out. The combination of SSAE-based similarity measurement criteria and deep transfer learning can reduce the differences between different domains. It is difficult to distinguish the data samples at the boundary and diagnose the problems that the physical meaning is difficult to understand. Through the bearing fault diagnosis analysis, the validity of the deep learning diagnosis model proposed in this paper is verified. The results show that the detection accuracy of the rolling bearing fault detection method based on LCM-SSAE is 0.6 percentage points higher than that of the rolling bearing fault detection method based on SSAE, which proves that the method is suitable for the fault detection of rolling bearing, and it also shows the effectiveness and robustness of the fault detection system of rolling bearing.

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Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Deep learning model.
Figure 2
Figure 2
Human brain mechanism.
Figure 3
Figure 3
Self-encoding network structure.
Figure 4
Figure 4
Autoencoding network.
Figure 5
Figure 5
Rolling bearing fault detection process based on stacked sparse self-encoding.
Figure 6
Figure 6
Logistic regression algorithm model.
Figure 7
Figure 7
Classification accuracy of different classifiers for each data type. (a) Type Of data. (b) Type of data.
Figure 8
Figure 8
Classification accuracy with different number of hidden layer nodes.
Figure 9
Figure 9
Classification accuracy of softmax classifier after SSAAE learning with different layers. (a) Type of data. (b) Type of data.
Figure 10
Figure 10
Detection accuracy of different classifiers combined with different network structures. (a) Classifier. (b) Classifier.
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