Deep Transfer Learning Based Fault Diagnosis for Electromagnetic Pulse Valve Faults Under Small Sample

The electromagnetic pulse valve, as a key component in baghouse dust removal systems, plays a crucial role in the performance of the system. However, despite the promising results of intelligent fault diagnosis methods based on extensive data in diagnosing electromagnetic valves, real-world diagnostic scenarios still face numerous challenges. Collecting fault data for electromagnetic pulse valves is not only time-consuming but also costly, making it difficult to obtain sufficient fault data in advance, which poses challenges for small sample fault diagnosis. To address this issue, this paper proposes a fault diagnosis method for electromagnetic pulse valves based on deep transfer learning and simulated data. This method achieves effective transfer from simulated data to real data through four parameter transfer strategies, which combine parameter freezing and fine-tuning operations. Furthermore, this paper identifies a parameter transfer strategy that simultaneously fine-tunes the feature extractor and classifier, and introduces an attention mechanism to integrate fault features, thereby enhancing the correlation and information complementarity among multi-sensor data. The effectiveness of the proposed method is evaluated through two fault diagnosis cases under different operating conditions. In this study, small sample data accounted for 7.9% and 8.2% of the total dataset, and the experimental results showed transfer accuracies of 93.5% and 94.2%, respectively, validating the reliability and effectiveness of the method under small sample conditions.