Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment
Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2024/6452897 |
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| _version_ | 1849308545561919488 |
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| author | Yaohua Deng Xiali Liu Zilin Zhang Daolong Zeng |
| author_facet | Yaohua Deng Xiali Liu Zilin Zhang Daolong Zeng |
| author_sort | Yaohua Deng |
| collection | DOAJ |
| description | Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection. |
| format | Article |
| id | doaj-art-160a1093c06c4c19a1b427ce3423e6e8 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-160a1093c06c4c19a1b427ce3423e6e82025-08-20T03:54:25ZengWileyShock and Vibration1875-92032024-01-01202410.1155/2024/6452897Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation EnvironmentYaohua Deng0Xiali Liu1Zilin Zhang2Daolong Zeng3School of Electromechanical EngineeringSchool of Electromechanical EngineeringSchool of Electromechanical EngineeringSchool of Electromechanical EngineeringAiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection.http://dx.doi.org/10.1155/2024/6452897 |
| spellingShingle | Yaohua Deng Xiali Liu Zilin Zhang Daolong Zeng Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment Shock and Vibration |
| title | Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment |
| title_full | Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment |
| title_fullStr | Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment |
| title_full_unstemmed | Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment |
| title_short | Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment |
| title_sort | mechanical fault sound source localization estimation in a multisource strong reverberation environment |
| url | http://dx.doi.org/10.1155/2024/6452897 |
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