Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder
To address the issue of insufficient abnormal sound samples for coal mine belt conveyors, which makes it difficult for training models to recognize anomalies, an abnormal sound detection method for coal mine belt conveyors based on Convolutional Autoencoder (CAE) is proposed. First, sound signals fr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2025-02-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2023090025 |
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| Summary: | To address the issue of insufficient abnormal sound samples for coal mine belt conveyors, which makes it difficult for training models to recognize anomalies, an abnormal sound detection method for coal mine belt conveyors based on Convolutional Autoencoder (CAE) is proposed. First, sound signals from the normal operation of the belt conveyor's idlers, reducer, and motor were collected. Background noise in the signals was filtered using the WebRTC noise reduction algorithm, and Mel-Frequency Cepstral Coefficients (MFCC) were calculated from the denoised signals to obtain audio features of normal operation. These features were then input into the CAE for training, resulting in a trained CAE and reconstructed audio features of normal operation. Next, the normal operation audio features and the reconstructed normal operation audio features were input into the Mean Squared Error Loss function (MSELoss) to obtain the reconstruction error, with the maximum reconstruction error set as the reconstruction threshold for normal operation audio features. Then, sound signals from the operation of the coal mine belt conveyor's idlers, reducer, and motor to be inspected were collected. After noise reduction using WebRTC and MFCC feature extraction, they were input into the trained CAE to obtain the reconstructed audio features of the inspected samples. The inspected audio features and the reconstructed audio features were then input into the MSELoss to calculate the reconstruction error of the inspected audios. Finally, the reconstruction error of the test audio was compared with the reconstruction threshold of normal operation audio features. If the former exceeded the latter, the coal mine belt conveyor was identified as abnormal. Experimental results showed that, without abnormal sound samples involved in training, the proposed method achieved detection accuracies of 92.55%, 94.98%, and 93.60% for the idlers, reducer, and motor, respectively. The detection time for a single sound sample was 1.230 seconds, achieving a balance between detection accuracy and speed. |
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| ISSN: | 1671-251X |