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Fault Diagnosis Method and Application Based on Multi-scale Neural Network and Data Enhancement for Strong Noise

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

The mechanical fault diagnosis method based on deep learning mainly uses single-scale convolution kernels to extract fault features, which is difficult to extract fault feature information comprehensively. Under strong noise conditions, the performance of fault diagnosis using single-scale convolution kernels will decrease sharply.

Methods

An intelligent fault diagnosis method based on multi-scale deep convolution neural network (MSD-CNN) model and data enhancement for strong noise is proposed in this paper. By the multi-scale cascade convolution kernels in the MSD-CNN, the multi-scale information of the original fault signal is extracted, and the ELU activation function is used to retain the negative information contained in the multi-scale information. By the data enhancement method for strong noise, the number and diversity of the MSD-CNN model training samples are improved, which enables the model to learn deeper features in the training stage.

Results and Conclusion

Compared six common fault diagnosis models, the proposed model achieves the optimal diagnostic accuracy of 99.98% and 99.66% under normal conditions and strong noise conditions, respectively, which verifies the effectiveness of the proposed method.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52075350), the Major Science and Technology Projects of Sichuan Province (No. 22ZDZX0001), and the Special City School Strategic Cooperation Project of Sichuan University and Zigong (No. 2021CDZG-3).

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Authors and Affiliations

  1. School of Mechanical Engineering, Sichuan University, Chengdu, 610065, China

    Zhehui Shao, Wenqiang Li, Hai Xiang, Shixiang Yang & Ziqi Weng

  2. Innovation Method and Creative Design Key Laboratory of Sichuan Province, Chengdu, 610065, China

    Zhehui Shao, Wenqiang Li, Hai Xiang, Shixiang Yang & Ziqi Weng

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  1. Zhehui Shao
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  2. Wenqiang Li
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Correspondence to Wenqiang Li.

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Shao, Z., Li, W., Xiang, H. et al. Fault Diagnosis Method and Application Based on Multi-scale Neural Network and Data Enhancement for Strong Noise. J. Vib. Eng. Technol. 12, 295–308 (2024). https://doi.org/10.1007/s42417-022-00844-x

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