Dual-Channel Acoustic Temporal–Spectral Representation and Noise-Perturbation Learning for Mining Conveyor Fault Diagnosis
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Published
Aug 3, 2026
Zhenyu Wang
Taixiang Li
Abstract
This study develops a robust acoustic fault diagnosis framework for mining conveyor idlers, addressing the challenge of detecting early-stage mechanical degradation in noisy and imbalanced industrial environments. A dual-channel temporal--spectral representation is constructed by combining multi-scale log-Mel spectrograms and raw waveforms to capture complementary spectral patterns and fine-grained temporal dynamics. A Dual-Stream Cross-Attention Convolutional Recurrent Neural Network (DSCA-CRNN) is proposed to model cross-stream dependencies and enhance feature fusion. Noise-perturbation augmentation and a triplet-based contrastive objective are employed to enrich minority fault samples and improve embedding discriminability. Experiments on a self-collected conveyor auscultation dataset with 2,495 segments across three health states show that DSCA-CRNN achieves an overall accuracy of 0.95 and a macro-F1 score of 0.90, outperforming representative machine learning and deep learning baselines. Severe-fault recognition reaches an F1-score of 0.80. Ablation studies and PCA visualization confirm the effectiveness of recurrent temporal modeling, cross-attention fusion, noise-perturbation learning, and contrastive representation shaping. The proposed auscultation-based framework provides a practical and deployable solution for safety-oriented conveyor condition monitoring.
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Keywords
Acoustic signal processing, Conveyor fault diagnosis, Deep learning, Contrastive learning, Noise augmentation, Automation
References
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Wang, M., Shen, K., Tai, C., Zhang, Q., Yang, Z., & Guo, C. (2023). Research on fault diagnosis system for belt conveyor based on internet of things and the lightgbm model. Plos one, 18(3), e0277352.
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Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning (pp. 27268–27286).
Bishop, C. M. (1995). Training with noise is equivalent to tikhonov regularization. Neural computation, 7(1), 108–116.
Bzinkowski, D., Rucki, M., Chalko, L., Kilikevicius, A., Matijosius, J., Cepova, L., & Ryba, T. (2024). Application of machine learning algorithms in real-time monitoring of conveyor belt damage. Applied Sciences, 14(22), 10464.
Chen, W., Shen, J., Zhu, H., & Xu, P. (2025). Interpretable fault diagnosis of coal-mine conveyor-belt idler bearings using multi-source fusion diagrams and mobile residual soft threshold–mobilevit. Ain Shams Engineering Journal, 16(12), 103777.
Erol, M. H., Senocak, A., Feng, J., & Chung, J. S. (2024). Audio mamba: Bidirectional state space model for audio representation learning. IEEE Signal Processing Letters, 31, 2975–2979.
Farhat, M. H., Gelman, L., Abdullahi, A. O., Ball, A., Conaghan, G., & Kluis, W. (2023). Novel fault diagnosis of a conveyor belt mis-tracking via motor current signature analysis. Sensors, 23(7), 3652.
Gong, Y., Chung, Y.-A., & Glass, J. (2021). Ast: Audio spectrogram transformer. arXiv preprint arXiv:2104.01778.
Han, C., Sim, Y., Jung, H., Lee, J., Lee, H., Kang, Y. S., . . . Jun, M. B.-G. (2025). Impact: Industrial machine perception via acoustic cognitive transformer. arXiv preprint arXiv:2507.06481.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778).
Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36–63.
Khalifa, R. M., Yacout, S., Bassetto, S., & Shaban, Y. (2025). Condition monitoring and warning of a belt drive system based on a logical analysis of data regression-based residual control chart. Structural Health Monitoring, 24(3), 1657–1673.
Kim, J., Yi, I., & Suh, Y.-J. (2025). Domain generalized open-set fault detection and diagnosis for belt conveyor systems with prototype learning. IEEE Access.
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137–3147.
Li, X., Wu, D., Liu, Y., & Chen, Y. (2024). Belt conveyor idler fault diagnosis method based on multi-scale feature fusion and residual mask convolution attention. Insight-Non-Destructive Testing and Condition Monitoring, 66(2), 82–93.
Li, Z., Wang, H., Liang, W., & Yao, L. (2024). Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments. Measurement Science and Technology, 35(10), 106126.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the ieee international conference on computer vision (pp. 2980–2988).
Liu, J., Fu, S., Liu, F., & Cheng, X. (2025). Intelligent fault diagnosis of belt conveyor rollers using a polar knn algorithm with audio features. Engineering Failure Analysis, 168, 109101.
Liu, Y., Miao, C., Li, X., Ji, J., Meng, D., & Wang, Y. (2023). A dynamic self-attention-based fault diagnosis method for belt conveyor idlers. Machines, 11(2), 216.
Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2022). A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730.
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019). Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779.
Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023). Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors, 23(3), 1305.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53–65.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 815–823).
Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence, 39(11), 2298–2304.
Soares, J. L., Costa, T. B., do Nascimento, G. S., Sousa, W. S., de Figueiredo, J. M., Braga, D. S., . . . Mesquita, A. L. (2025). Decision tree and anova as feature selection from vibration signals to improve the diagnosis of belt conveyor idlers. Signals, 6(3), 42.
Soares, J. L., Costa, T. B., Moura, L. S., Sousa, W. S., Mesquita, A. L., Mesquita, A. L., . . . Braga, D. S. (2024). Fault diagnosis of belt conveyor idlers based on gradient boosting decision tree. The International Journal of Advanced Manufacturing Technology, 132(7), 3479–3488.
Sung, S.-H., Hong, S., Choi, H.-R., Park, D.-M., & Kim, S. (2024). Enhancing fault diagnosis in iot sensor data through advanced preprocessing techniques. Electronics, 13(16), 3289.
Wang, J., Mo, Z., Zhang, H., & Miao, Q. (2019). A deep learning method for bearing fault diagnosis based on
time-frequency image. IEEE Access, 7, 42373–42383.
Wang, M., Shen, K., Tai, C., Zhang, Q., Yang, Z., & Guo, C. (2023). Research on fault diagnosis system for belt conveyor based on internet of things and the lightgbm model. Plos one, 18(3), e0277352.
Wu, H., Hu, T., Liu, Y., Zhou, H.,Wang, J., & Long, M. (2022). Timesnet: Temporal 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186.
Wu, X., Wang, C., Tian, Z., Huang, X., & Wang, Q. (2023). Research on belt deviation fault detection technology of belt conveyors based on machine vision. Machines, 11(12), 1039.
Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical systems and signal processing, 100, 439–453.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning (pp. 27268–27286).
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