A novel method for fault diagnosis of analog circuits based on WP and GPNN

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet (WP) decomposition and probabilistic neural networks using genetic algorithm (GPNN) is proposed in this paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit being solved. Then by fusing various uncertain factors into probabilistic operations, GPNN methods to diagnose faults are proposed whose parameters and structure obtained form genetic optimisations resulting in best detection of faults. Finally, simulations indicated that GPNN classifiers are correct 7% more than BPNN of the test data associated with our sample circuits.

[1]  Farzan Aminian,et al.  A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor , 2007, IEEE Transactions on Instrumentation and Measurement.

[2]  Masashi Hayakawa,et al.  Identification of electric circuits described by ill-conditioned mathematical models , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Farzan Aminian,et al.  Analog fault diagnosis of actual circuits using neural networks , 2002, IEEE Trans. Instrum. Meas..

[4]  Dominik R. Dersch,et al.  Multiresolution Forecasting for Futures Trading , 2001 .

[5]  Martin Margala,et al.  Defect detection in analog and mixed circuits by neural networks using wavelet analysis , 2005, IEEE Transactions on Reliability.

[6]  J. Rutkowski,et al.  New concept to analog fault diagnosis by creating two fuzzy-neutral dictionaries test , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[7]  Jacob Savir,et al.  Coefficient-based test of parametric faults in analog circuits , 2003, Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412).

[8]  Janusz A. Starzyk,et al.  A unified decomposition approach for fault location in large analog circuits , 1984 .

[9]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[10]  Kuo-Chung Lin,et al.  Wavelet packet feature extraction for vibration monitoring , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[11]  R. Picos,et al.  Fault Detection and Parameter Prediction of an OpAmp using a Charge Monitor , 2006, 2006 International Caribbean Conference on Devices, Circuits and Systems.

[12]  Yuen-Haw Chang,et al.  Robust fault diagnosis for large-scale analog circuits with measurement noises , 1997 .

[13]  Zhihua Wang,et al.  Probabilistic fault detection and the selection of measurements for analog integrated circuits , 1998, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[14]  M. Kowalewski.  Two-center Radial Basis Function Network For Classification of Soft Faults in Electronic Analog Circuits , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[15]  Jacquelien M. A. Scherpen,et al.  Fault detection method for nonlinear systems based on probabilistic neural network filtering , 2002, Int. J. Syst. Sci..

[16]  Zhen Guo,et al.  Coefficient-based test of parametric faults in analog circuits , 2006, IEEE Transactions on Instrumentation and Measurement.

[17]  Kenji Fukumizu,et al.  Probabilistic design of layered neural networks based on their unified framework , 1995, IEEE Trans. Neural Networks.

[18]  Junfang Liu,et al.  Researches on Soft Fault Diagnosis Algorithm of Analogy Circuits Based on DDAGSVMs , 2007, 2007 IEEE International Conference on Integration Technology.