水电能源科学

针对孤立森林算法固定阈值导致复杂工况下检测准确度降低的问题，提出一种基于奇异谱分析(SSA)与改进孤立森林(KMIF)的船闸人字门监测数据异常检测方法。利用SSA对监测数据进行分解与重构，分离趋势项和噪声项；引入K-Means++改进孤立森林算法(IF),动态设定不同监测数据集的异常阈值；将噪声项输入改进的孤立森林算法进行训练并检测异常值。以江苏船闸工程下闸首人字门的多测点应力、振动数据为对象进行实例验证。结果表明，提出的奇异谱分析-改进孤立森林方法(SSA-KMIF)在误检率、查准率、查全率和准确率指标上表现优异，具有较高准确性和灵活性，可为船闸人字门健康监测提供可靠技术支撑。

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.