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Fault Diagnosis of Motor Bearing Equipment Based on Sound Signal

Published: 10 April 2023 Publication History

Abstract

In the diagnostic technology of the motor, the sound of the motor at work contains a lot of useful information. The identification of the sound signal can reflect the running status of the motor, so that the parts with problems can be repaired. In this paper, a fault diagnosis device based on sound signal is designed for bearings with high damage rate in motor. Wavelet analysis will be used to threshold de-noise the collected signal, then BP and RBF neural networks will be built and trained to compare which of the two networks is better. The results show that both neural networks can accurately identify the sound signals of different types of bearings, but the training speed of RBF neural networks is faster.

References

[1]
Wei Yonghe, Gong Junyu. Fault diagnosis of rolling bearing based on CNN-LSTM Attention [J]. Journal of Shenyang University of Technology, 2022,41 (04): 73-77.
[2]
Huang Xinzhe Research on machine fault abnormal sound recognition technology based on deep learning [D]. Guilin University of Technology, 2021.
[3]
Zheng Siyu. Mechanical equipment fault diagnosis based on sound signal [J]. Internal combustion engine and accessories, 2021 (13): 129-132.
[4]
Sun Yongming Research on belt conveyor fault diagnosis system based on sound signal [D]. Qufu Normal University, 2021.
[5]
Su Xinmei. Research on Sound Signal Recognition by MATLAB [J]. Electronic World, 2021 (09): 36-37.
[6]
Ling Biaocan, Yang Jiabin. Comparison of BP and RBF neural networks in motor rolling bearing fault diagnosis [J]. Journal of North China University of Science and Technology, 2018,15 (06): 53-57.
[7]
Liu Yanjie, Chen Bingfa, Ding Liping. Fault diagnosis method of micromotor based on acoustic characteristics [J]. Mechanical Manufacturing and Automation, 2022,51 (02): 190-194.
[8]
Huang Xinzhe Research on machine fault abnormal sound recognition technology based on deep learning [D]. Guilin University of Technology, 2021.
[9]
Zhu Min, Deng Wei, Zhao Li. A Convolutional Neural Network Based Method for Environmental Sound Classification [J]. Electronic Devices, 2021,44 (02): 423-427.
[10]
Bai Lin, Huang Ziyu, Ye Cheng, Jiang Yingying. Vehicle voice signal recognition based on BP neural network [J]. Automation Technology and Application, 2014, 33 (02): 64-66+86.

Cited By

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  • (2024)Time-Shift Denoising Combined With DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current SignalsIEEE Sensors Journal10.1109/JSEN.2024.345509924:21(35019-35035)Online publication date: 1-Nov-2024

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ICITEE '22: Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering
November 2022
739 pages
ISBN:9781450396806
DOI:10.1145/3582935
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2023

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Author Tags

  1. BP neural network
  2. RBF neural network
  3. Sound signal
  4. Wavelet analysis
  5. fault diagnosis

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View all
  • (2024)Time-Shift Denoising Combined With DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current SignalsIEEE Sensors Journal10.1109/JSEN.2024.345509924:21(35019-35035)Online publication date: 1-Nov-2024

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