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To address the issue of reduced detection accuracy under complex working conditions due to the fixed threshold of the isolation forest algorithm, an anomaly detection method for ship lock miter gate monitoring data based on singular spectrum analysis (SSA) and an improved isolation forest (KMIF) is proposed. The SSA is employed to decompose and reconstruct the monitoring data, and separate the trend and noise components. The isolation forest algorithm is improved by incorporating K-Means++ clustering to dynamically set anomaly thresholds for different monitoring datasets. The noise component is then fed into the improved isolation forest algorithm for training and anomaly detection. Taking the stress and vibration data from multiple measuring points of the lower lock miter gate in Jiangsu ship gate project as an example for validation, the results show that the proposed SSA-KMIF method performs excellently in terms of false positive rate, precision, recall ratio, and accuracy. It demonstrates high accuracy and flexibility, which provides a reliable technical support for health monitoring of ship lock miter gates.
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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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