A literature review of fault diagnosis based on ensemble learning

Mian, Zhibao; Deng, Xiaofei; Dong, Xiaohui; Tian, Yuzhu; Cao, Tianya; Chen, Kairan; Jaber, Tareq Al

Abstract

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance.

Citation

Mian, Z., Deng, X., Dong, X., Tian, Y., Cao, T., Chen, K., & Jaber, T. A. (2024). A literature review of fault diagnosis based on ensemble learning. Engineering applications of artificial intelligence, 127, Article 107357. https://doi.org/10.1016/j.engappai.2023.107357

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2023
• Online Publication Date: Nov 10, 2023
• Publication Date: Jan 1, 2024
• Deposit Date: Oct 23, 2023
• Publicly Available Date: Nov 13, 2023
• Journal: Engineering Applications of Artificial Intelligence
• Print ISSN: 0952-1976
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 127
• Article Number: 107357
• DOI: https://doi.org/10.1016/j.engappai.2023.107357
• Keywords: Ensemble learning; Fault diagnosis; Intelligent maintenance; System reliability
• Public URL: https://hull-repository.worktribe.com/output/4423723