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Outline
Anomaly detection in gravity dam monitoring data is critical for dam safety assessment. To overcome limitations of existing methods in pattern identification and feature extraction, this paper proposes a hybrid anomaly pattern detection method based on VMD-SSA-KMeans-iForest. By introducing variational mode decomposition (VMD) to optimize the decomposition process, the accuracy and robustness of feature extraction is significantly improved. Furthermore, an anomaly detection framework is established by combining K-means with isolation forest. This method is applied to the identification of abnormal data of the W gravity dam. The results demonstrate that the proposed method achieves a 2.5% improvement in anomaly detection accuracy while effectively distinguishing between anomalies caused by structural damage and those resulting from instrument failure,which provides more reliable technical support for the safety assessment of gravity dams.
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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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