A new switched current circuit fault diagnosis approach based on pseudorandom test and preprocess by using entropy and Haar wavelet transform

This paper presents a new switched current (SI) circuit fault diagnosis approach based on pseudorandom test and preprocess by using entropy and Haar wavelet transform. The proposed method has the capability to detect and identify faulty transistors in SI circuit by analyzing its time response. The use of pseudorandom sequences as a stimulate signal to SI circuit reduces the cost of testing and the overhead of the test generation circuit, and using entropy and Haar wavelet transform to preprocess the time response for feature extraction drastically improves the fault diagnosis efficiency. For both actual experiment and analysis of switched current filters in Z transform (ASIZ) simulation, a low-pass, a band-pass SI filter and a clock feed-through cancellation circuit have been used as test examples to verify the effectiveness of the proposed method. The result shows that the accuracy of fault recognition achieved is about 100% by analyzing low-frequency approximations entropy and high-frequency details entropy. Therefore, it indicates that the presented method is superior than other methods.