Abstract
This paper proposed a new fault diagnosis method based on the extension theory for analog circuits. The responses of an analog circuit were difference at some node with the normal and failure conditions. However, the identification of the faulted location was not easily task due to the variability of circuit components. So this paper presented a novel extension method for fault diagnosis of analog circuit, which is based on the matter-element model and extended relation functions. The proposed method has been tested on a practical analog circuit, and compared with the multilayer neural network (MNN) based methods and k-means classification method. The application of this new method to some testing cases has given promising results.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yongkui, S., Guangju, C., Hui, L.: Analog Circuits Fault Diagnosis Using Support Vector Machine. In: International Conf. on Communications, Circuits and Systems, 2007. ICCCAS 2007, pp. 1003–1006 (2007)
Catelani, M., Fort, A., Alippi, C.: A Fuzzy Approach for Soft Fault Detection in Analog Circuits. Meas. 32, 73–83 (2002)
Slamani, M., Kaminska, B.: Fault Observability Analysis of Analog Circuits in Frequency Domain. IEEE Trans. on Circuits Systems II, Analog and Digital Signal Proc., 134–139 (1996)
Tadeusiewicz, M., Halgas, S., Korzybski, M.: An Algorithm for Soft-Fault Diagnosis of Linear and Nonlinear Circuits. IEEE Trans. on Circuits and Systems, Fundamental Theory and Applications 49(11), 1648–1653 (2002)
Catelani, M., Fort, A.: Soft Fault Detection and Isolation in Analog Circuits: some Results and a Comparison between a Fuzzy Approach and Radial Basis Function Networks. IEEE Trans. on Instrumentation and Meas. 51, 196–202 (2002)
Yanghong, T., Yigang, H., Chun, C., Guanyuan, Q.: A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms. IEEE Trans. on Nstrumentation and Meas. 57(11), 2631–2639 (2008)
Spina, R., Upadhyaya, S.: Linear Circuit Fault Diagnosis Using Neuron Morphed Analyzers. IEEE Trans. on Circuits Systems II, Analog Digital Signal Proc. 44(3), 188–196 (1997)
Wang, M.H.: A Novel Extension Method for Transformer Fault Diagnosis. IEEE Trans. on Power Delivery 18(1), 164–169 (2002)
Wang, M.H.: Application of Extension Theory to Vibration Fault Diagnosis of Generator Sets. IEE Proc. Generation, Transmission and Distribution 151(4), 503–508 (2004)
Cai, W.: The Extension Set and Incompatibility Problem. J. of Scientific Exploration 1, 81–93 (1983)
Cai, W.: Extension Set, Fuzzy Set and Classical Set. In: First Congress of International Fuzzy System Association, Spain (1985)
Li, J., Wang, S.: Primary Research on Extension Control Information System, vol. 1. International Academic Publishers (1991)
Huang, Y.P., Chen, H.J.: The Extension-Based Fuzzy Modeling Method and its Applications. In: IEEE Canadian Conf. on Electrical and Computer Engineering, vol. 2, pp. 977–982 (1999)
Huang, Y.C., Yang, H.T., Huang, C.L.: Developing a New Transformer Diagnosis System through Evolutionary Fuzzy Logic. IEEE Trans. on Power Delivery 12(2), 761–767 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, MH., Chung, YK., Sung, WT. (2009). The Fault Diagnosis of Analog Circuits Based on Extension Theory. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-04070-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
eBook Packages: Computer ScienceComputer Science (R0)