An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples

Yutong Dong, Hongkai Jiang, Xin Wang, Mingzhe Mu and Wenxin Jiang

Reliability Engineering and System Safety, 2024, vol. 251, issue C

Abstract: Previous deep learning-based fault diagnosis methods for planetary gearbox require numerous training samples and lack the necessary interpretability. Aiming at the problems of insufficient interpretability of deep models and the absence of feature mining capability with small samples, this study presents an interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis. First, an interpretable multiscale lifting wavelet network is designed to achieve comprehensive and credible features mining from fault signals. Secondly, an interactive channel attention mechanism is constructed to choose feature maps with different frequency components. It can further confirm the interpretability of the lifting wavelet layer while improving the accuracy of the model. Finally, a time-frequency contrast loss is designed to simultaneously optimizing the distribution of time-frequency domain features. The effectiveness and interpretability of the model are analyzed through various visualization approaches. Experimental results on two planetary gearbox datasets indicate that our method is an interpretable and effective fault recognition method with small samples, and it holds a promising future for engineering applications.

Keywords: Fault diagnosis; Small samples; Interpretable lifting wavelet layer; Interactive channel attention mechanism; Time-frequency supervised contrast learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832024004769
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004769

DOI: 10.1016/j.ress.2024.110404

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().