TY - JOUR
T1 - Analog fault diagnosis using S-transform preprocessor and a QNN classifier
AU - Tan, Yanghong
AU - Sun, Yichuang
AU - Yin, Xin
N1 - © 2013 Elsevier Ltd. All rights reserved.
PY - 2013/8/1
Y1 - 2013/8/1
N2 - 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.
AB - 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.
KW - Analog fault diagnosis
KW - Mixed-signal test
KW - Quantum neural network
KW - S-transform
UR - http://www.scopus.com/inward/record.url?scp=84877013474&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2013.03.002
DO - 10.1016/j.measurement.2013.03.002
M3 - Article
AN - SCOPUS:84877013474
SN - 0263-2241
VL - 46
SP - 2174
EP - 2183
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
IS - 7
ER -