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针对复杂控制系统的数据维度高、变量之间存在耦合和信息冗余严重的特点,采用动态主元分析和加权模糊C均值聚类相结合的方法.在考虑控制系统动态特性的基础上,降低数据维度;同时提取主元特征值作为权值系数,描述不同特征对控制系统故障的贡献程度.然后,采用模糊C均值聚类算法,获得正常数据的聚类中心,建立其与故障数据的加权差值模型,最后对控制系统进行故障检测.实验结果表明,该方法能提高控制系统故障检测的准确性和有效性.
In view of the high data dimension of the complex control system, the coupling between variables and the serious information redundancy, the dynamic principal component analysis and the weighted fuzzy C-means clustering method are combined. Based on the consideration of the dynamic characteristics of the control system, Data dimension.At the same time, the eigenvalue of the principal component is extracted as the weight coefficient to describe the contribution of different features to the fault of the control system.Furthermore, the fuzzy C-means clustering algorithm is used to obtain the clustering center of normal data, Finally, the fault detection of the control system is carried out.The experimental results show that this method can improve the accuracy and effectiveness of the fault detection of the control system.