Condition-Aware Performance Health Index and Multi-Source Signal Mapping for Degradation Trend Identification in Hydropower Units

Hydropower units often operate under complex conditions caused by water head change, guide-vane regulation, and load adjustment. These condition changes make it difficult to identify gradual performance degradation from monitoring signals alone. To solve this problem, this paper proposes a condition-aware performance health index construction and multi-source signal-mapping method for hydropower units. First, active power, guide-vane opening, and water head are used as the main operating variables. After data preprocessing and steady-state screening, water head is used as a prior constraint to divide the hydraulic boundary. FCM clustering is then used in each head layer to obtain different operating regions. Second, a high-quantile performance envelope is built in each operating region. The optimal active power is used as the performance benchmark, and the performance health index HIperf is constructed by comparing actual power with optimal power. The results show that the proposed method can describe the performance deviation under comparable operating conditions. The smoothed daily HIperf shows a degradation trend before maintenance and a recovery trend after maintenance. Finally, vibration and shaft-swing signals are mapped to HIperf to construct the signal-based health index HIsig. The mapping result shows good consistency between HIsig and HIperf, and shaft-swing features show stronger sensitivity than vibration features. The proposed framework focuses on daily-scale degradation trend identification using steady-state operating samples, while transient operating events are excluded from the current analysis. The proposed method provides a useful reference for degradation trend identification and health assessment of hydropower units under complex operating conditions.