CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains

Chang Guo, Zuogang Shang, Jiaxin Ren, Zhibin Zhao, Baoqing Ding, Shibin Wang and Xuefeng Chen

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

Abstract: Intelligent fault diagnosis (IFD) based on deep learning (DL) has demonstrated its powerful performance to promote the reliability and safe operation of rotating machinery. In industrial applications, working condition changes frequently, leading to models’ performance degeneration due to out-of-distribution (OOD) samples, such as vibration signals with unseen rotating speeds, loads, etc. To address this issue, domain generalization fault diagnosis (DGFD) has attracted increasing attention. However, mainstream DGFD methods are implemented from statical dependence, which fail to excavate intrinsic causal mechanisms and have limited performance and reliability. From a causal view, the causal independence and sparse shift network (CIS2N) is proposed to diagnose via causal mechanisms. Specially, a structural causal model (SCM) representing causal relations of features in IFD is given. Based on the SCM, it is derived that features are jointly independent and feature deviation between intervened sample pairs is sparse. The dependence among features is measured via the covariance matrices constructed by high-level features mapped by random Fourier features (RFF). Meanwhile the L1-distance between pairs of features with the same causal features (intervened pair) is calculated and optimized to realize shift sparsity. The effectiveness of the proposed CIS2N on DGFD is demonstrated on two real helical gearbox datasets.

Keywords: Domain generalization; Intelligent fault diagnosis; Causality; Deep learning; Gearbox (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832024004538
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:s0951832024004538

DOI: 10.1016/j.ress.2024.110381

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 ().