Pseudo-label Semi-supervised Fault Diagnosis Method for Application Abnormal Data
In addressing the challenges of large-scale data,high annotation costs,and imbalanced sample class distribution encountered in fault diagnosis tasks,a semi-supervised learning method for anomaly data-driven pseudo-labeling is proposed.Firstly,this method employs data augmentation on pseudo-labeled data and introduces a pseudo-label loss function for iterative model optimization.Additionally,an adaptive imbalance-aware network is designed,integrating an adaptive loss function to reduce the imbalance gap among samples and enhance model generalization.Finally,by employing a distribution alignment-based strategy,a selective pseudo-label self-training framework is constructed,effectively alleviating potential prediction drift issues during iterative training.Experimental results demonstrate significant performance improvement in fault diagnosis compared to traditional baseline semi-supervised learning algorithms,particularly on real-world disk datasets.
万方数据
维普
