Intelligent Diagnosis of Bearing Fault Based on Voiceprint

In order to solve the problem that the current bearing fault diagnosis model based on voiceprint signal is not enough to extract time features, a bearing fault diagnosis model based on 3DCNN is proposed in this paper. First, the Mel-spectrogram is used to extract the voiceprint of the bearing. Then,...

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Bibliographic Details
Main Authors: Deng, Yunpeng, Liang, Daili, Zhang, Yang, Chen, Xiaohong, Sun, Peipei, Jiao, Tinghao, Tang, Chong
Format: Conference Proceeding
Language:English
Subjects:
3DCNN
bearing fault diagnosis
Big Data
Business intelligence
deep learning
Fault diagnosis
Feature extraction
Machine learning
Machine learning algorithms
Mel-spectrogram
Timing
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Summary:In order to solve the problem that the current bearing fault diagnosis model based on voiceprint signal is not enough to extract time features, a bearing fault diagnosis model based on 3DCNN is proposed in this paper. First, the Mel-spectrogram is used to extract the voiceprint of the bearing. Then, the 3DCNN model proposed is used to diagnose the fault of the bearing to make better use of the timing information of the model. Finally, the model proposed in this paper has improved the precision and recall rate by 6.25% and 7.03% respectively compared with the current classical algorithm. The model has good accuracy and is important for engineering practice.
ISSN:2994-2977
DOI:10.1109/MLBDBI60823.2023.10481918