A fault diagnosis deep learning model based on feature extraction with good anti-noise ability

Pengfei Wei; Yong Zhao; Zhao An; Yuanjun Guo; Zhile Yang

doi:10.1117/12.3004745

20 October 2023 A fault diagnosis deep learning model based on feature extraction with good anti-noise ability

Pengfei Wei, Yong Zhao, Zhao An, Yuanjun Guo, Zhile Yang

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 1291615 (2023) https://doi.org/10.1117/12.3004745
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

This paper proposes a fault diagnosis model based on the combination of continuous wavelet transform and improved first-layer wide convolutional neural network (CWT-IWDCNN). The model first performs continuous wavelet transform on the data containing a lot of noise to extract its characteristic data, and converts the original data into a time-frequency map. Then, the time-frequency diagram is fed into the IWDCNN model to obtain the diagnosis result. The proposed CWT-IWDCNN has the following advantages. 1) The continuous wavelet transform can effectively extract fault features; 2) The first layer of the convolutional neural network uses a wide convolution kernel to suppress high-frequency noise, and other layers use small the convolution kernel improves the domain adaptive ability of the model through nonlinear mapping to improve the accuracy of diagnosis; Finally, in order to test the performance of this model, the CWRU dataset is used for experimental verification. The experiments show that the proposed fault diagnosis method is better than WDCNN in most cases in terms of accuracy and robustness.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengfei Wei, Yong Zhao, Zhao An, Yuanjun Guo, and Zhile Yang "A fault diagnosis deep learning model based on feature extraction with good anti-noise ability", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 1291615 (20 October 2023); https://doi.org/10.1117/12.3004745

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KEYWORDS

Data modeling

Performance modeling

Convolution

Continuous wavelet transforms

Feature extraction

Statistical modeling

Convolutional neural networks

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