Continual learning for fault diagnosis considering variable working conditions

Dongdong Wei, Ming Jian Zuo and Zhigang Tian

Journal of Risk and Reliability, 2025, vol. 239, issue 3, 584-595

Abstract: Traditional Neural Networks (NNs) trained in a one-stage process often struggle to perform well when presented with new classes or domain shifts in testing datasets. In fault diagnosis, it is essential to handle a sequence of diagnostic tasks with new fault classes and working conditions. This paper presents a multi-staged Continual Learning algorithm that learns from a sequence of diagnostic tasks. In each training stage, a small portion of previously seen training data is incorporated to help the model remember old tasks and better learn new tasks. A novel scheme is designed to select previously seen data from multiple old tasks, considering their different working conditions. A multi-way domain adaptation is then conducted to mitigate the impact of multiple changes in working conditions among different tasks. The proposed method is tested using two different experiment test rigs, including both gear and bearing faults. Results demonstrate that the proposed Continual Learning algorithm allows NNs to learn from a sequence of diagnostics tasks efficiently and maintain high accuracies for all the tasks of interest.

Keywords: Gearbox; fault diagnostics; vibration signals; continual learning; domain adaptation; machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X241252469 (text/html)

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:sae:risrel:v:239:y:2025:i:3:p:584-595

DOI: 10.1177/1748006X241252469

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().