Journal
ISA TRANSACTIONS
Volume 119, Issue -, Pages 152-171Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2021.02.042
Keywords
Intelligent fault diagnosis; Small & imbalanced data; Data augmentation; Feature learning; Classifier design; Meta-learning; Zero-shot learning
Categories
Funding
- National Natural Science Foundation of China [91960106, 51875436, U1933101, 61633001, 51421004, 51965013]
- China Postdoctoral Science Foundation [2020T130509, 2018M631145]
- Shaanxi Natural Science Foundation, China [2019JM-041]
Ask authors/readers for more resources
Research on intelligent fault diagnosis using artificial intelligence technologies has achieved significant progress, particularly in the field of S&I-IFD. Existing strategies include data augmentation, feature learning, and classifier design. Future research directions involve meta-learning and zero-shot learning.
The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available