Author:
Luo Haihang,Tang Chunqiu,Yu Yongsheng
Publisher
Springer Nature Switzerland
Reference15 articles.
1. Yan, Y., Xing, J., Xie, M.: Research on bearing fault diagnosis based on SPWVD and grid optimization CNN. In: 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), pp. 1014–1018. Shenyang, China (2023)
2. Mao, Y., Liao, X.:Bearing Fault diagnosis based on convolution neural network with multi-attention mechanism. In: 2023 4th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM), pp. 210–214. Nanjing, China (2023)
3. Zhang,W., Peng, G., Li, C., Chen, Y., Zhang, Z.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2) (2017)
4. Xiong, H., Wang, Z., Wu, G., Pan, Y., Yang, Z., Long, Z.: Steering actuator fault diagnosis for autonomous vehicle with an adaptive denoising residual network. IEEE Trans. Instrum. Meas. 71, 1–13 (2022)
5. Wang, H., Liu, Z., Peng, D., Qin, Y.: Understanding and learning discriminant features based on multi-attention 1DCNN for wheelset bearing fault diagnosis. IEEE Trans. Industr. Inf. 16(9), 5735–5745 (2020)