Fault diagnosis method for hydropower units based on deep transfer learning networks

Abstract

[Objective] Due to the limited fault data during actual operation of hydropower units and differences in fault signal distributions across different units that do not conform to the assumption of identical distribution, model training becomes difficult, and existing diagnostic models have poor generalization capabilities. To address these issues, a fault diagnosis method combining transfer learning strategies with a CNN-BIGRU-attention network(TCBA) is proposed. [Methods] Rotor test bench data was used as the source domain, and real vibration data from hydropower units served as the target domain data. First, a CNN-BIGRU-attention diagnostic model was constructed by combining Convolutional Neural Network(CNN), Bidirectional Gated Recurrent Unit(BIGRU), and attention units. The model was initially trained using source domain data, and its parameters were then transferred to the fault diagnosis model of the target domain. During the transfer process, the lower-layer network was frozen, and the upper-layer network was fine-tuned using partial target domain data, resulting in a fault diagnosis model adapted for the target equipment. To verify the effectiveness of the proposed method, a comparison was conducted between the proposed method and traditional deep learning method through transfer experiments using rotor test bench datasets and real hydropower unit fault data, evaluating indicators such as recognition accuracy, training speed, and sample size requirements. [Results] The result showed that, compared with traditional training method, the proposed method significantly improved the model's convergence speed and effectively reduced the sample size required for training. Under small sample conditions, the fault state recognition accuracy for actual hydropower station fault sample data reached 99.02%, which was about 3% higher than that of the traditional method. [Conclusion] This study demonstrates that the proposed method has strong fault state recognition capabilities, providing an effective solution for fault diagnosis of hydropower units under limited data conditions.

Keywords

hydropower station / hydropower units / vibration signals / fault diagnosis / transfer learning / data-driven