A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm
The wayside acoustic defective bearing detector system (TADS) is located on both sides of the railway, so that the acoustic signals recorded by the microphone not only include the sound from the train bearings but also include it from the other disturbance sources. The heavy noise and multisource ac...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/8303722 |
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| _version_ | 1849402323222134784 |
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| author | Yali Sun Hua Li Xing Zhao Jiyou Fei Xiaodong Liu Yijie Niu |
| author_facet | Yali Sun Hua Li Xing Zhao Jiyou Fei Xiaodong Liu Yijie Niu |
| author_sort | Yali Sun |
| collection | DOAJ |
| description | The wayside acoustic defective bearing detector system (TADS) is located on both sides of the railway, so that the acoustic signals recorded by the microphone not only include the sound from the train bearings but also include it from the other disturbance sources. The heavy noise and multisource acoustic signals would badly reduce the reliability and accuracy of the detection result of the TADS. In order to extract the useful information from the recorded signal exactly and efficiently, a novel denoising method based on the Short-time Fourier transform (STFT) and improved Crazy Climber algorithm was improved in this paper. Firstly, the STFT was performed on the recorded acoustic signals in order to obtain the time-frequency distribution matrix. Based on the original algorithm, the novel movement rule and the fitting process of the ridge lines were presented which could extract the time-frequency ridge lines of the acoustic signal accurately and rapidly. In this way, the important information from the train bearings could be divided from the heavy noise and other signals. Finally, the simulation and experimental verifications were carried out, and the denoising method based on the STFT and improved Crazy Climber algorithm has proved to be effective in extracting ridge lines of the time-frequency distribution matrix and dividing the useful information form the recorded acoustic signals. |
| format | Article |
| id | doaj-art-8b321f2fff354701b9cd225d7671a1c9 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-8b321f2fff354701b9cd225d7671a1c92025-08-20T03:37:33ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/8303722A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber AlgorithmYali Sun0Hua Li1Xing Zhao2Jiyou Fei3Xiaodong Liu4Yijie Niu5College of Mechanical EngineeringCollege of Locomotive and RollingCollege of Locomotive and RollingCollege of Locomotive and RollingCollege of Locomotive and RollingCollege of SoftwareThe wayside acoustic defective bearing detector system (TADS) is located on both sides of the railway, so that the acoustic signals recorded by the microphone not only include the sound from the train bearings but also include it from the other disturbance sources. The heavy noise and multisource acoustic signals would badly reduce the reliability and accuracy of the detection result of the TADS. In order to extract the useful information from the recorded signal exactly and efficiently, a novel denoising method based on the Short-time Fourier transform (STFT) and improved Crazy Climber algorithm was improved in this paper. Firstly, the STFT was performed on the recorded acoustic signals in order to obtain the time-frequency distribution matrix. Based on the original algorithm, the novel movement rule and the fitting process of the ridge lines were presented which could extract the time-frequency ridge lines of the acoustic signal accurately and rapidly. In this way, the important information from the train bearings could be divided from the heavy noise and other signals. Finally, the simulation and experimental verifications were carried out, and the denoising method based on the STFT and improved Crazy Climber algorithm has proved to be effective in extracting ridge lines of the time-frequency distribution matrix and dividing the useful information form the recorded acoustic signals.http://dx.doi.org/10.1155/2022/8303722 |
| spellingShingle | Yali Sun Hua Li Xing Zhao Jiyou Fei Xiaodong Liu Yijie Niu A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm Shock and Vibration |
| title | A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm |
| title_full | A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm |
| title_fullStr | A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm |
| title_full_unstemmed | A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm |
| title_short | A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm |
| title_sort | novel denoise method of acoustic signal from train bearings based on resampling technique and improved crazy climber algorithm |
| url | http://dx.doi.org/10.1155/2022/8303722 |
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