改进的模糊聚类在控制系统故障诊断中的应用

为了提高控制系统中传感器与执行器故障诊断的准确性,结合小波分析特征提取的优势和密度函数加权模糊C-均值聚类具有较好分类效果的特点,提出了一种新的控制系统故障诊断方法。该方法首先利用小波分析对故障信号进行特征提取,降低噪声的影响;然后对特征提取后的数据通过加权模糊C-均值聚类算法,对故障进行识别分类。实验表明,基于小波分析和加权模糊C-均值聚类相结合的方法,不仅可以识别不同部件的故障,而且可以对同一部件的不同类型的故障进行诊断。

Control system fault diagnosis using improved fuzzy clustering

In order to improve the fault diagnosis accuracy of the sensor and actuator of the control system, we propose a new control system fault diagnosis method by combining the advantages of wavelet analysis for extracting features with the good clustering effect of the fuzzy C-means algorithm based on a weighted density function. Firstly, we use wavelet analysis to extract the features of fault signals to reduce the influence of noise. Secondly, we employ the fuzzy C-means clustering algorithm to classify the data whose features have been extracted. Experimental results show that the proposed algorithm can not only identify the fault of different components, but also diagnose different types of faults on the same part.