ResNet + Self-Attention-Based Acoustic Fingerprint Fault Diagnosis Algorithm for Hydroelectric Turbine Generators

作者：Wei Wang, Jiaxiang Xu, Xin Li, Kang Tong, Kai-lun SHI, Xin Mao, Junxue Wang, Yunfeng Zhang, Liao Yong · 发表于：Processes · 年份：2025 · DOI：10.3390/pr13082577 · 被引用次数：2 · 研究领域：Hydraulic and Pneumatic Systems、Machine Fault Diagnosis Techniques、Cavitation Phenomena in Pumps

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm...