TDMSAE: A transferable decoupling multi-scale autoencoder for mechanical fault diagnosis. (15th February 2023)

• Record Type: Journal Article
• Authors: Yu, Shihang; Wang, Min; Pang, Shanchen; Song, Limei; Zhai, Xue; Zhao, Yawu
• Is Part Of: Mechanical systems and signal processing. Volume 185(2023)
• Publication Date: 2023-02-15
• Subjects: Intelligent fault diagnosis -- Artificial intelligence -- Multi-scale -- Transferable; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109789
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: When the working environment of mechanical equipment changes, the fault diagnosis model trained in the source domain cannot adapt well to the new environment, meaning the model drift has occurred. This paper proposes a transferable decoupling multi-scale autoencoder (TDMSAE) to address the above problem. Firstly, the feature extraction module is constructed using the multi-scale residual network, which can extract features of vibration signals at different scales by setting the different sizes of convolution kernels. Secondly, the distribution alignment module based on the transposed convolution is constructed to offset the distribution discrepancy of the source and target domains by transferring features and parameters of the feature extraction module so that the fault diagnosis model can achieve higher accuracy in the target domain. Finally, the experimental results show that the TDMSAE, compared to the other three existing methods, achieves a maximum of 100% accuracy when the model is transferred from the source domain to the target domain in eight sub-datasets.