基于PCA加权及模糊聚类的设备运行监测模型

Equipment Operation Monitoring Model Based on PCA Weighting and Fuzzy Clustering

设计一种新的模型,根据电厂设备的运行参数为其赋分,判断其健康状态。首先运用主成分分析法计算各运行参数在判断设备健康状态时所占的权重,然后通过加权欧式距离法进行模糊均值聚类,建立设备健康诊断模型,计算设备的健康得分。测试结果表明,通过该模型计算得到的设备状态与实际情况高度吻合,能准确表征设备状态,并对设备的异常状态提前发出报警。

A new model is designed to assign scores to power plant equipment based on its operating parameters to assess its state of health.Firstly,the principal component analysis method is applied to calculate the weight of each operating parameter in deter⁃mining the equipment's health condition.Then,fuzzy C-means clustering is performed using the weighted Euclidean distance method to establish an equipment health diagnosis model,which can calculate the health score of the equipment.Test results have shown that the equipment condition calculated by this model highly corresponds with the actual condition,accurately representing the equipment status,and providing early warnings for abnormal equipment conditions.