In the context of highly imbalanced sample data in bearing health monitoring,this article proposes an a-nomaly detection approach employing a generative adversarial network(GAN).The model's training necessitates solely the utilization of samples from normal states.Subsequently,an anomaly detection threshold is determined through a grid search method.Following this,a similarity assessment is conducted on the detected faults.Finally,the effectiveness of the method is validated using a publicly available dataset.Experimental results demonstrate that this approach achieves a detection accuracy of over 95%for various faults and can provide assessments of the severity of these faults.
关键词
滚动轴承/不平衡样本/异常检测/生成对抗网络
Key words
Rolling bearings/Imbalanced data/Anomaly detection/Generative adversarial networks
维普
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