Practical Analog Circuit Diagnosis Based on Fault Features with Minimum Ambiguities
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- 作者：Xiaofeng Tang ; Aiqiang Xu
- 关键词：Analog circuit diagnosis ; Ambiguity model ; Optimal fault feature ; Maximum likelihood classifier ; ANNs ; SVM
- 刊名：Journal of Electronic Testing
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：32
- 期：1
- 页码：83-95
- 全文大小：1,108 KB
- 参考文献：1.Alippi C, Catelani M, Fort A, Mugnaini M (2002) SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms. IEEE Trans Instrum Meas 51(5):1116–1125CrossRef
2.Aminian M, Aminian F (2000) Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circuits Syst II, Analog Digit Signal Process 47(2):151–156CrossRef
3.Ao YC, Shi YB, Zhang W, Li YJ (2013) An approximate calculation of ratio of normal variables and its application in analog circuit fault diagnosis. J Electron Test 29(4):555–565CrossRef
4.Bowman AW (1984) An alternative method of cross-validation for the smoothing of density estimates. Biometrika 71(2):353–360CrossRef MathSciNet
5.Catelani M, Fort A (2000) Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement 28(3):147–158CrossRef
6.Cui J, Wang YR (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44(1):281–289CrossRef
7.Deng Y, Shi YB, Zhang W (2012) An approach to locate parametric faults in nonlinear analog circuits. IEEE Trans Instrum Meas 61(2):358–367CrossRef
8.Gao C, Huang JY, Sun Y, Diao SL (2012) Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis. J Cent South Univ 19:459–464CrossRef
9.Han H, Wang HJ, Tian SL, Zhang N (2013) A new analog circuit fault diagnosis method based on improved mahalanobis distance. J Electron Test 29(1):95–102CrossRef
10.Haykin S (2009) Neural networks and learning machines, third edition. Pearson Education, Upper Saddle River
11.Hu M, Wang H, Hu G, Yang SY (2007) Soft fault diagnosis for analog circuits based on slope fault feature and BP neural networks. Tsinghua Sci Technol 12:26–31CrossRef
12.Huang K, Stratigopoulos HG, Mir S (2010) Bayesian fault diagnosis of RF circuits using nonparametric density estimation. Proc IEEE Asian Test Symp:295–298
13.Huang K, Stratigopoulos HG, Mir S, Hora C, Xing YZ, Kruseman B (2012) Diagnosis of local spot defects in analog circuits. IEEE Trans Instrum Meas 61(10):2701–2712CrossRef
14.Jia Y, Harman M (2011) An analysis and survey of the development of mutation testing. IEEE Trans Softw Eng 37(5):649–678CrossRef
15.Jones MC, Marron JS, Sheather SJ (1996) A brief survey of bandwidth selection for density estimation. J Am Stat Assoc 91(433):401–407CrossRef MathSciNet MATH
16.Kumar A, Singh AP (2013) Fuzzy classifier for fault diagnosis in analog electronic circuits. ISA Trans 52 (6):816–824CrossRef
17.Litovski V, Andrejević M, Zwolinski M (2006) Analogue electronic circuit diagnosis based on ANNs. Microelectron Reliab 46(8):1382–1391CrossRef
18.Luo H, Wang YR, Cui J (2011) A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor. Expert Syst Appl 38(8):10554– 10561CrossRef
19.Luo H, Wang YR, Lin H, Jiang YY (2012) Module level fault diagnosis for analog circuits based on system identification and genetic algorithm. Measurement 45(4):769–777CrossRef
20.Luo H, Wang YR, Lin H, Jiang YY (2012) A new optimal test node selection method for analog circuit. J Electron Test 28(3):279–290CrossRef
21.Sarathi Vasan AS, Long B, Pecht M (2013) Diagnostics and prognostics method for analog electronic circuits. IEEE Trans Ind Electron 60(11):5277–5291CrossRef
22.Tan H, Peng MF (2012) Minimization of ambiguity in parametric fault diagnosis of analog circuits: A complex network approach. Appl Math Comput 219(1):408–415CrossRef MathSciNet MATH
23.Taylor CC (1989) Bootstrap choice of the smoothing parameter in kernel density estimation. Biometrika 76 (4):705–712CrossRef MathSciNet MATH
24.Xie T, He YG (2014) Fault diagnosis of analog circuit based on high-order cumulants and information fusion. J Electron Test 30(5):505–514CrossRef
25.Xu AQ, Tang XF, Yang ZY (2014) Mutant generation for analog circuit designs. Proc IEEE Inter Conf Soft Eng Service Sci:916–920
26.Yuan LF, He YG, Huang JY, Sun YC (2010) A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans Instrum Meas 59(3):586–595CrossRef - 作者单位：Xiaofeng Tang (1)
Aiqiang Xu (1)
1. Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai, 264001, Shandong, China - 刊物类别：Engineering
- 刊物主题：Circuits and Systems
Electronic and Computer Engineering
Computer-Aided Engineering and Design - 出版者：Springer Netherlands
- ISSN：1573-0727
文摘
As numerous faults exist in practical analog circuits, new challenges arise in the field of diagnosis with large-scale target faults as well as fault features. To address this issue, firstly, an ambiguity model is built to measure the distinguishability between two faults. Then, the optimal fault features are obtained by analyzing the response curves of the circuit under test (CUT) to minimize the ambiguities among the faults. Finally, comparisons are made among three classification methods, including the maximum likelihood classifier (MLC), artificial neural networks (ANNs) and support vector machine (SVM), to demonstrate their own diagnostic abilities for practical use. Two examples are illustrated, and taking advantage of an automated implementation framework, 92 faults in total are examined in the second example. The experimental results show that good diagnostic performances can be obtained with the proposed method. However, when a practical case is encountered, the ANNs method may fail due to its high time and space complexity, while the MLC and SVM methods are still applicable. Keywords Analog circuit diagnosis Ambiguity model Optimal fault feature Maximum likelihood classifier ANNs SVM