Analog fault diagnosis using S-transform preprocessor and a QNN classifier

Abstract

A novel method for fault diagnosis in analog circuits using S-transform (ST) as a preprocessor and a quantum neural network (QNN) as a classifier is proposed in this paper. The ST provides a frequency-dependent resolution and the features obtained from ST are distinct, and easy to understand. The QNN identifier, a computational tool for fuzzy classification combining the advantages of neural modeling and fuzzy-theoretic principles, has the ability to autonomously detect the presence of uncertainty, adaptively learn the existing uncertainty, properly approximate any membership profile, and autonomously quantify uncertainty in sample data. The comparison between the ST-based method and the wavelet-transform-based method, and comparison between the QNN method and the traditional NN method for analog fault diagnosis is provided. Simulation results show that the proposed method is effective in enhancing the efficiency of the training phase and the performance of the fault diagnostic system. The results clearly indicate more than 97.61% correct classification of fault classes in the example circuits of various sizes in the presence of similar faults.