Bearing fault diagnosis based on dual-stream CNN using wavelet time-frequency FFT spectrum

Mingshen Xu; Tianyi Li; Po Guan; Xinzhuo Shen; Yu Fu

doi:10.1117/12.3033775

13 June 2024 Bearing fault diagnosis based on dual-stream CNN using wavelet time-frequency FFT spectrum

Mingshen Xu, Tianyi Li, Po Guan, Xinzhuo Shen, Yu Fu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131802A (2024) https://doi.org/10.1117/12.3033775
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

The article proposes a method for bearing fault diagnosis based on the waveform FFT spectrum of the dual-stream CNN. The method first uses FFT and wavelet transform to obtain the one-dimensional FFT spectrum and two-dimensional time-frequency map of bearing vibration, and then inputs them into the 1D-CNN channel and 2D-CNN channel of the model for feature extraction. After the feature information is fused and processed, the fault diagnosis is finally completed in the classification layer. The experimental results show that this model significantly improves the accuracy of bearing fault diagnosis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingshen Xu, Tianyi Li, Po Guan, Xinzhuo Shen, and Yu Fu "Bearing fault diagnosis based on dual-stream CNN using wavelet time-frequency FFT spectrum", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131802A (13 June 2024); https://doi.org/10.1117/12.3033775

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