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In hydropower station monitoring systems， fixed threshold methods are commonly used for over-limit alarms， but they exhibit low sensitivity in complex conditions， making early warnings difficult. This paper proposes a feature-enhanced anomaly detection （FEAD-DEGAN） model based on generative adversarial network discriminator and density estimation （DEGAN）. Convolution and global average pooling methods optimize the discriminator structure， enhancing time-series feature extraction. The dynamic threshold strategy and kernel density estimation improve detection sensitivity. The model is validated with abnormal oil head swing amplitude data from an axial-flow pump-turbine unit. Compared with Isolation Forest and Autoencoder， the proposed approach shows better performance in anomaly detection success rate and false alarm rate. SHAP quantifies the contribution of monitoring indicators to anomalies， identifying key factors that influence abnormal behavior and enhancing process interpretability. This supports root cause analysis and facilitates the optimization of maintenance strategies， thereby contributing to more effective fault diagnosis and intelligent maintenance.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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