Abstract
This paper presents a novel method that can detect component faults in analog circuits. Because the probability density function (PDF) of output voltage (current) is sensitive to the components of the circuit, the cross-entropy between the good circuit and the bad circuit is employed to detect component faults in analog circuits based on the autoregressive (AR) model. In the proposed approach, the value of each component of the circuit undertest (CUT) is varied within its tolerance limit using Monte Carlo simulation. The minimal and maximal bounds of the cross-entropy are found for fault-free circuit. While testing, the cross-entropy is obtained. If cross-entropy lies outside the tolerance limit then the CUT is declared faulty. The effectiveness of the proposed method is demonstrated via the second order Sallenkey bandpass filter circuit and continuous-time low pass state-variable filter circuit.
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References
Aminian F, Aminian M, Collins H (2002) Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas 51(3):544–550
Bandler J, Salama A (1985) Fault diagnosis of analog circuits. Proc IEEE 73:1279–1325
Butcher S, Sheppard J (2009) Distributional smoothing in bayesian fault diagnosis. IEEE Trans Instrum Meas 58(2):342–349
Deng Y, Shi Y, Zhang W (2011) An approach to locate parametric faults in nonlinear analog circuits. IEEE Trans Instrum Meas 61(2):358–367
Iuculano G, Nielsen L, Zanobini A, Pellegrini G (2007) The principle of maximum entropy applied in the evaluation of the measurement uncertainty. IEEE Trans Instrum Meas 56(3):717–722
Kay S (1988) Modern spectral estimation: theory and applications, ch 6. Prentice-Hall, Englewood Cliffs, NJ
Kay S (1998) Model-based probability density function estimation. IEEE Signal Process Lett 5(12):318–320
Kaminska B, Arabi K, Bell I, Goteti P, Huertas JL, Kin B, Rueda A, Soma M (1997) Analog and mixed-signal benchmark circuits - first release In: Proc IEEE int test conf (online), pp 183–190
Kavithamani A, Manikandan V, Devarajan N (2012) Fault detection of analog circuits using network parameters. Int J Electron Test Theory Appl, Springer 28:257–261
Kullback S (1968) Information theory and statistics, ch 1. Dover Publications, New York
Papakostas D, Hatzopoulos A (1993) Correlation-based comparison of analog signatures for identification and fault diagnosis. IEEE Trans Instrum Meas 42(4):860–863
Papoulis A (1965) Probability, random variables and stochastic processes, ch 10. McGraw-Hill, New York
Shore J, Johnson R (1980) Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Trans Inf Theory (IT) 26:26–37
Stefan V, Omar E, Colin T, Frank O (2011) Challenges for semiconductor test engineering: a review paper. JETTA Springer 28(3):365–374
Stoica P, Selen R (2004) A review of information criterion rules. IEEE Signal Process Mag 21(4):36–47
Tzannes M, Politis D, Tzannes N (1985) A general method of minimum cross-entropy spectral estimation. IEEE Trans Acoust Speech Signal Process (ASSP) 33(3):748–752
Yang C, Tian S, Long B, Chen F (2011) Methods of handling the tolerance and test-point selection problem for analog-circuit fault diagnosis. IEEE Trans Instrum Meas 60(1):176–185
Yang T, Yi H, Chun C, Guan Q (2008) A novel method for analog fault diagnosis based on neural networks and genetic algorithms. IEEE Trans Instrum Meas 57(11):2631–2639
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Responsible Editor: K. K. Saluja
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Li, X., Xie, Y. Analog Circuits Fault Detection Using Cross-Entropy Approach. J Electron Test 29, 115–120 (2013). https://doi.org/10.1007/s10836-012-5344-x
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DOI: https://doi.org/10.1007/s10836-012-5344-x