ResearchArticle SBT Approach towards Analog Electronic Circuit Fault Diagnosis

An approach for the fault diagnosis of single and multiple faults in linear analog electronic circuits is proposed in this paper. The simulation-before-test (SBT) diagnosis approach proposed in this write up basically consists of obtaining the frequency response of fault free/faulty circuit. The peak frequency and the peak amplitude from the error response are observed and processed suitably to extract distinct signatures for faulty and nonfaulty conditions under maximum tolerance conditions for other network components. The artificial neural network classifiers are then used for the classification of fault. Networks of reasonable dimensions are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances. This is a unique contribution of this paper. Fault computation time is drastically reduced from the traditional analysis techniques. This results in a direct dollar savings at test time. A comparison of the proposed work with the previous works which also employ preprocessing techniques, reveals that our algorithm performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.

[1]  Lee A. Feldkamp,et al.  Automotive diagnostics using trainable classifiers: statistical testing and paradigm selection , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  Ada Fort,et al.  Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy approach and radial basis function networks , 2002, IEEE Trans. Instrum. Meas..

[3]  E. Zafiriou,et al.  Use of neural networks for sensor failure detection in a control system , 1990, IEEE Control Systems Magazine.

[4]  F. Aminian,et al.  Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor , 2000 .

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[6]  Ruey-Wen Liu Selected papers on analog fault diagnosis , 1987 .

[7]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[8]  Richard Lippmann,et al.  Review of Neural Networks for Speech Recognition , 1989, Neural Computation.

[9]  Heikki N. Koivo,et al.  Application of artificial neural networks in process fault diagnosis , 1991, Autom..

[10]  J.W. Bandler,et al.  Fault diagnosis of analog circuits , 1985, Proceedings of the IEEE.

[11]  M. F. Abu El-Yazeed,et al.  A Preprocessor for Analog Circuit Fault Diagnosis Based on Prony's Method , 2003 .

[12]  Shambhu Upadhyaya,et al.  Linear circuit fault diagnosis using neuromorphic analyzers , 1997 .

[13]  Ruey-Wen Liu Testing and Diagnosis of Analog Circuits and Systems , 1991, Springer US.

[14]  T. A. Grogan,et al.  Comparative analysis of five neural network models , 1992 .

[15]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[16]  H. F. Spence Automatic analog fault simulation , 1996, Conference Record. AUTOTESTCON '96.