184 / 2024-09-01 16:22:25

An Improved Semi-Supervised Prototype Network For Few-Shot Fault Diagnosis

intelligent diagnosis, semi-supervised learning, meta-learning, standard euclidean distance, few-shot.

Special Sessions > SS03 Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries

The collected data for labeled ones in transient nature of mechanical faults are limitation in practical engineering scenarios. However, the completeness of sample determines quality for feature information, which is extracted by deep learning network. Therefore, to obtain more effective information with limited data, this paper proposes an improved semi-supervised prototype network (ISSPN) that can be used for fault diagnosis. Firstly, a meta-learning strategy is used to divide the sample data. Then, a standard Euclidean distance metric is used to improve the SSPN, which maps the samples to the feature space and generates prototypes. Further, the original prototypes are processed with the help of unlabeled data to purify better prototypes. Finally, the classifier clusters the various faults. The effectiveness of the proposed method is verified through experiments. The experimental results show that the proposed method can do a better job of classifying different faults.