Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects

Abstract

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its strong feature representation capability in recent years. Nevertheless, in engineering scenarios, machines often operate under normal condition and the probability of various faults is also different, which leads to the extreme class imbalance and long-tailed distribution between different health conditions. Long-tailed distribution usually degrades the performance of the model, since the model tends to focus on dominant classes and exhibits a poor performance on tail classes. To address this problem, numerous researches have been carried out and fruitful achievements have been achieved in recent years. However, a comprehensive summary of the latest achievements is still lacking. In addition, existing studies solely concentrate on the issue of class imbalance, and lack of analysis and future research directions of long-tailed problem. Hence, this paper specifically defines the long-tailed distribution and discusses the relations between long-tailed problem and class imbalance problem. Then, research achievements on class imbalance problems are reviewed. Specifically, we divide the existing research achievements into three categories: class rebalance, information augmentation and module design. Finally, this paper summarizes the challenges of the long-tailed problem in fault diagnosis and provides some future research directions.