A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

Authors

  • Weihai Sun  Zhongshan Comprehensive Energy Service Co., Ltd, Zhongshan, Guangdong, China
  • Lemei Han  College of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China

DOI:

https://doi.org//10.32628/IJSRSET207430

Keywords:

Sound Signal, Fault Detection, Time Domain Analysis, Frequency Domain Analysis

Abstract

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

References

  1. Hao Hongtao, Ni Fanfan, Ding Wenjie. Idler fault diagnosis method based on sound signal [ J ] . Noise and vibration control, 2019,39(003) : 187-192.
  2. Yang Yuanwei, Guan Yonggang, Chen Shigang, Wang Jingjun, Zhao Ke. Mechanical fault diagnosis method of high voltage circuit breaker based on sound signal [J]. Chinese Journal of electrical engineering, 2018,38 (22): 6730-6737
  3. Serban G , Iana G V , Anghelescu P . Fault detection of direct radiator loudspeaker systems by phase characterisation[C]// 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2016.
  4. Shen Bowen, Wang Huaqing, Tang Gang, song Liuyang. Acoustic signal fault feature extraction method based on MCKD and ceemdan [J]. Fudan Journal: Natural Science Edition, 2019,58 (3): 385-392
  5. Akcay H , Germen E . Identification of acoustic spectra for fault detection in induction motors[C]// Africon. IEEE, 2013.
  6. Altaf M , Uzair M , Naeem M , et al. Automatic and Efficient Fault Detection in Rotating Machinery using Sound Signals[J]. Acoustics Australia, 2019.
  7. He H , Wang C , Wu Y . [IEEE 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE 2010) - Changchun, China (2010.08.24-2010.08.26)] 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering - Application of wavelet transform in sound source detection and fault diagnostic[J]. 2010.
  8. Feng Tao, Wang Jie, Fang Xia, Liu Jiange, Huang Sisi. Fault diagnosis of micro vibration motor based on CNN and sound time frequency characteristic graph [J]. China test, 2019,45 (10): 120-127.
  9. Amarnath, Muniyappa. Local Fault Assessment in a Helical Geared System via Sound and Vibration Parameters Using Multiclass SVM Classifiers[J]. Archives of acoustics, 2016.
  10. Li X , Liu Z , Qu Y , et al. Unsupervised Gear Fault Diagnosis Using Raw Vibration Signal Based on Deep Learning[C]// 2018 Prognostics and System Health Management Conference (PHM-Chongqing). 2019.
  11. Qu Y , Zhang Y , He M , et al. Gear pitting fault diagnosis using disentangled features from unsupervised deep learning[J]. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 2019.
  12. Yang Y , Guan Y , Chen S , et al. Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Sound Signal[J]. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2018, 38(22):6730-6736.
  13. Jonguk, Lee, Heesu. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.[J]. Sensors (Basel, Switzerland), 2016.
  14. Ehsan M , David W , Qiao S . Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration[J]. Energies, 2017, 10(11):1853.
  15. Luo L , Liu X , Yan X . Insulation fault identification of vacuum circuit breakers based on improved MFCC and SVM[J]. Journal of Physics: Conference Series, 2019, 1176:062027.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Weihai Sun, Lemei Han, " A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 4, pp.129-139, July-August-2020. Available at doi : https://doi.org/10.32628/IJSRSET207430