A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions

方位(导航) 断层(地质) 机器学习 计算机科学 样品(材料) 人工智能 领域(数学) 模式识别(心理学) 数据挖掘 工程类 数学 色谱法 地质学 地震学 化学 纯数学
作者
Hao Su,Ling Xiang,Aijun Hu,Yonggang Xu,Xin Yang
出处
期刊:Mechanical Systems and Signal Processing [Elsevier]
卷期号:169: 108765-108765 被引量:123
标识
DOI:10.1016/j.ymssp.2021.108765
摘要

Recently, intelligent fault diagnosis has made great achievements, which has aroused growing interests in the field of bearing fault diagnosis due to its strong feature learning ability. Sufficient bearing fault samples are taken for granted in existing intelligent fault diagnosis methods generally. In practice, however, the lack of fault samples has been a knotty problem. Therefore, in this paper, a novel method called data reconstruction hierarchical recurrent meta-learning (DRHRML) is proposed for bearing fault diagnosis with small samples under different working conditions. This approach contains data reconstruction and meta-learning stages. In the data reconstruction stage, noise is reduced and the useful information hidden in the raw data is extracted. In the meta-learning stage, the proposed method is trained by a recurrent meta-learning strategy with one-shot learning way. This approach is demonstrated on the bearing fault database with 92 working conditions from Case Western Reserve University and with 56 working conditions from laboratory. Results show that the proposed method is effective for bearing intelligent fault diagnosis with small samples under different working conditions.
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