A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing
Increased attention has been paid to research on intelligent fault diagnosis under acoustic signals. However, the signal-to-noise ratio of acoustic signals is much lower than vibration signals, which increases the difficulty of signal denoising and feature extraction. To solve the above defect, a no...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8837047 |
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| _version_ | 1832566339835789312 |
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| author | Guowei Zhang Jinrui Wang Baokun Han Sixiang Jia Xiaoyu Wang Jingtao He |
| author_facet | Guowei Zhang Jinrui Wang Baokun Han Sixiang Jia Xiaoyu Wang Jingtao He |
| author_sort | Guowei Zhang |
| collection | DOAJ |
| description | Increased attention has been paid to research on intelligent fault diagnosis under acoustic signals. However, the signal-to-noise ratio of acoustic signals is much lower than vibration signals, which increases the difficulty of signal denoising and feature extraction. To solve the above defect, a novel batch-normalized deep sparse filtering (DSF) method is proposed to diagnose the fault through the acoustic signals of rotating machinery. In the first stage, the collected acoustic signals are prenormalized to eliminate the adverse effects of singular samples, and then the normalized signal is transformed into frequency-domain signal through fast Fourier transform (FFT). In the second stage, the learned features are obtained by training batch-normalized DSF with frequency-domain signals, and then the features are fine-tuned by backpropagation (BP) algorithm. In the third stage, softmax regression is used as a classifier for heath condition recognition based on the fine-tuned features. Bearing and planetary gear datasets are used to validate the diagnostic performance of the proposed method. The results show that the proposed DSF model can extract more powerful features and less computing time than other traditional methods. |
| format | Article |
| id | doaj-art-64385064a96b4b17a54705f778eec3ca |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-64385064a96b4b17a54705f778eec3ca2025-02-03T01:04:23ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88370478837047A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal ProcessingGuowei Zhang0Jinrui Wang1Baokun Han2Sixiang Jia3Xiaoyu Wang4Jingtao He5College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, ChinaIncreased attention has been paid to research on intelligent fault diagnosis under acoustic signals. However, the signal-to-noise ratio of acoustic signals is much lower than vibration signals, which increases the difficulty of signal denoising and feature extraction. To solve the above defect, a novel batch-normalized deep sparse filtering (DSF) method is proposed to diagnose the fault through the acoustic signals of rotating machinery. In the first stage, the collected acoustic signals are prenormalized to eliminate the adverse effects of singular samples, and then the normalized signal is transformed into frequency-domain signal through fast Fourier transform (FFT). In the second stage, the learned features are obtained by training batch-normalized DSF with frequency-domain signals, and then the features are fine-tuned by backpropagation (BP) algorithm. In the third stage, softmax regression is used as a classifier for heath condition recognition based on the fine-tuned features. Bearing and planetary gear datasets are used to validate the diagnostic performance of the proposed method. The results show that the proposed DSF model can extract more powerful features and less computing time than other traditional methods.http://dx.doi.org/10.1155/2020/8837047 |
| spellingShingle | Guowei Zhang Jinrui Wang Baokun Han Sixiang Jia Xiaoyu Wang Jingtao He A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing Shock and Vibration |
| title | A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing |
| title_full | A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing |
| title_fullStr | A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing |
| title_full_unstemmed | A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing |
| title_short | A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing |
| title_sort | novel deep sparse filtering method for intelligent fault diagnosis by acoustic signal processing |
| url | http://dx.doi.org/10.1155/2020/8837047 |
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