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.