Fault Diagnosis for Hydropower Units Based on Multi-Sensor Data with Multi-Scale Fusion

Accurate fault diagnosis of hydropower units is crucial for ensuring the efficient and complete utilization of hydropower resources. Existing diagnostic methods predominantly consider either single-sensor or single-scale multi-sensor fusion, failing to fully exploit the effective information within monitoring data. Furthermore, they neglect the correlation between different sensors and faults during fusion diagnosis, thereby limiting the diagnostic performance of fusion models. To address this, this paper proposes a multi-sensor data fault diagnosis method based on multi-scale fusion. First, a feature extraction model is constructed to extract shallow-level features from multi-sensor signals across multiple dimensions. Subsequently, an attention-based feature fusion network is designed to extract and fuse multi-depth features, yielding high-quality deep-fused features. Finally, an information-entropy-based decision fusion strategy is established to effectively enhance the model’s diagnostic performance. Experimental validation on the public rotating machinery fault dataset and the hydropower unit fault dataset yielded diagnostic accuracies of 96.42% and 99.28%, respectively, demonstrating the significant effectiveness and robustness of the proposed method.