Continuous Hidden Markov Model Based Incipient Fault Monitoring in Filtered Analog Circuits

Abstract:

The small variations of components parameters often lead to severe performance degradation in Filtered Analog Circuits (FAC). Most of the researches on soft fault diagnosis in analog circuit are focused on the variations beyond 30% of the nominal parameter in recent years, which is usually unacceptable in FAC. To diagnose the soft fault of small deviation as earlier as possible, the Hidden Markov Model (HMM) was introduced to monitor the FAC. Different from the existing diagnosis approaches based on HMM, in which the variations of components parameters were considered to be random, the continuous variations of the fault component parameter are discretized and modeled by the hidden states of the proposed HMM method. The experiment demonstrates that the proposed HMM approach can model the FAC effectively and recognize the incipient fault earlier.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 181-186
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.181
• Online since: August 2013
• Authors: Yong Cai Ao, Yi Bing Shi, Wei Zhang, Yan Jun Li
• Keywords: Continuous Hidden Markov Model, Filtered Analog Circuits, Incipient Fault, Tolerance

References

[1] Grzechca, D. (2011). Soft fault clustering in analog electronic circuits with the use of self organizing neural network. Metrol. Meas. Syst., 18(4), 555-568.

DOI: 10.2478/v10178-011-0054-8

[2] Chaojie, Z., Guo, H., Shuhai, L. (2008). Test point selection of analog circuits based on fuzzy theory and ant colony algorithm. IEEE Autotestcon, 164-168.

DOI: 10.1109/autest.2008.4662605

[3] Wang, P., Yang, S. (2006). A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math. IEEE Trans. on Circuits and Systems, 52(10), 2118-2127.

DOI: 10.1109/tcsi.2005.853266

[4] Zhou, L., Shi, Y., Tang, J., Li, Y. (2009). Soft Fault Diagnosis in Analog Circuit Based on Fuzzy and Direction Vector. Metrol. Meas. Syst., 16(1), 61-75.

[5] Y. Ao, Y. Shi, W. Zhang, X. Li (2012). A Novel Method of Handling Tolerances for Analog Circuit Fault Diagnosis Based on Normal Quotient Distribution. Metrol. Meas. Syst. , Vol. XIX, No. 4, pp.817-830.

DOI: 10.2478/v10178-012-0073-0

[6] B. Long and S. Tian and Q. Miao and Pecht, M(2011). Research on features for diagnostics of filtered analog circuits based on LS-SVM. AUTOTESTCON, IEEE, pp.360-366.

DOI: 10.1109/autest.2011.6058746

[7] Rabiner, Lawrence R(1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), pp.257-286.

DOI: 10.1109/5.18626

[8] L. Wang, Mehrabi, Mostafa G, Kannatey-Asibu, Elijah(2002). Hidden Markov model-based tool wear monitoring in turning. Transactions - American Society of Mechanical Engineer Journal of Manufacturing Science and Engineering. 124(3), pp.651-658.

DOI: 10.1115/1.1475320

[9] Xu, L. J., Huang, J.G., Wang, H.J., Long, B. (2010). A novel method for the diagnosis of the incipient faults in analog circuits based on LDA and HMM. Circuits Syst Signal Process, 29, 577-600.

DOI: 10.1007/s00034-010-9160-1

[10] Y. Deng, Y. Shi, W. Zhang(2012). Diagnosis of incipient faults in nonlinear analog circuits. Metrol. Meas. Syst., XIX(2), pp.203-218.

DOI: 10.2478/v10178-012-0018-7

[11] Chung, K.L. (2001). A Course in Probability Theory. America, Elsevier, Inc.