Algorithm

Engineering

Classifier (UML)

Data mining

Computer science

Fault (geology)

Seismology

Geology

Artificial intelligence

Machine learning

State (computer science)

ISA Transactions

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.

Intelligent fault diagnosis of machines with small &amp; imbalanced data: A state-of-the-art review and possible extensions

Intelligent fault diagnosis of machines with small &amp; imbalanced data: A state-of-the-art review and possible extensions

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions