Review of Research on Bearing Fault Diagnosis with Small Samples

doi:10.3778/j.issn.1002-8331.2208-0139

Abstract

Abstract: With the advent of the data era, bearing fault diagnosis methods based on data-driven have shown superior performance, but such methods rely on a large number of labeled data, and it is difficult to collect a large amount of data in the actual production process, so bearing fault diagnosis with small samples has high research value. In this paper, the bearing fault diagnosis methods under the condition of small samples are reviewed, and divided into two categories：data-based methods and model-based methods. The data-based method expands the original samples from the perspective of data. The model-based methods refer to the use of models to optimize feature extraction or improve classification accuracy. Finally, the shortcomings of current fault diagnosis methods under the condition of small samples are summarized, and future research directions of bearing fault diagnosis with small samples are prospected.

Key words: small samples, fault diagnosis, data expansion, meta-learning, transfer learning

摘要： 随着数据时代的来临，基于数据驱动的轴承故障诊断方法表现出了优越的性能，但是此类方法依赖大量标记数据，而在实际生产过程中很难收集到大量的数据，因此小样本的轴承故障诊断具有很高的研究价值。对小样本条件下的轴承故障诊断方法进行了回顾，并将其分为两类：基于数据的方法和基于模型的方法。其中基于数据的方法是从数据角度对原始样本进行扩充；基于模型的方法是指利用模型优化特征提取或者提高分类精度等。总结了当前小样本条件下故障诊断方法的不足，并展望了小样本轴承故障诊断的未来。

关键词: 小样本, 故障诊断, 数据扩充, 元学习, 迁移学习

SI Weiwei, CEN Jian, WU Yinbo, HU Xueliang, HE Minzan, YANG Zhuohong, CHEN Honghua. Review of Research on Bearing Fault Diagnosis with Small Samples[J]. Computer Engineering and Applications, 2023, 59(6): 45-56.

司伟伟, 岑健, 伍银波, 胡学良, 何敏赞, 杨卓洪, 陈红花. 小样本轴承故障诊断研究综述[J]. 计算机工程与应用, 2023, 59(6): 45-56.

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