基于多核多分类相关向量机的模拟电路故障诊断方法
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摘要
针对模拟电路实际存在的多类故障问题,本文提出一种基于多核多分类相关向量机(Multi-kernel learning multiclass relevance vector machine, MKL-mRVM)的模拟电路故障诊断方法.所提方法能够在故障数据所在的原始特征空间上建立多个非线性核,在构建分类器的同时实现故障特征的约简;同时,基于贝叶斯框架的分类模型还能够给出诊断结果的后验概率.通过两个电路的诊断实验证明了所提方法的优越性和实用性.
Aimed at the problem of multi-class fault diagnosis in practical analog circuits, a new diagnostic method based on multi-kernel learning multiclass relevance vector machine(MKLmRVM) is proposed. The proposed method can build multikernels in the feature space where fault data are originally represented, which can realize the reduction of fault features during the modeling of classifier. In addition, the classifier based on Bayesian framework is able to output the posterior probability of diagnostic results. The fault diagnostic results of two circuits demonstrate the advantage and practicability of the proposed method.
Aimed at the problem of multi-class fault diagnosis in practical analog circuits, a new diagnostic method based on multi-kernel learning multiclass relevance vector machine(MKLmRVM) is proposed. The proposed method can build multikernels in the feature space where fault data are originally represented, which can realize the reduction of fault features during the modeling of classifier. In addition, the classifier based on Bayesian framework is able to output the posterior probability of diagnostic results. The fault diagnostic results of two circuits demonstrate the advantage and practicability of the proposed method.
引文
1 Sarathi Vasan A S, Long B, Pecht M. Diagnostics and prognostics method for analog electronic circuits. IEEE Transactions on Industrial Electronics, 2013, 60(11):5277-5291
2 Ao Y C, Shi Y B, Zhang W, Li Y J. An approximate calculation of ratio of normal variables and its application in analog circuit fault diagnosis. Journal of Electronic Testing Theory&Applications, 2013, 29(4):555-565
3 Tadeusiewicz M, Halgas S. A new approach to multiple soft fault diagnosis of analog BJT and CMOS Circuits. IEEE Transactions on Instrumentation&Measurement, 2015,64(10):2688-2695
4 Tang X F, Xu A Q. Practical analog circuit diagnosis based on fault features with minimum ambiguities. Journal of Electronic Testing, 2016, 32(1):83-95
5 Liu Z B, Liu T M, Han J W, Bu S H, Tang X J, Pecht M.Signal Model-based fault coding for diagnostics and prognostics of analog electronic circuits. IEEE Transactions on Industrial Electronics, 2016, 64(1):605-614
6 Jing Y P, Yuan L F, He Y G. An analog circuit diagnosis method based on Adaptive-Kernel ICA. International Journal of Electronics and Electrical Engineering, 2016, 4(2):116-120
7 Luo H, Wang Y R, Lin H, Jiang Y Y. A new optimal test node selection method for analog circuit. Journal of Electronic Testing, 2012, 28(3):279-290
8 Tan H, Peng M F. Minimization of ambiguity in parametric fault diagnosis of analog circuits:a complex network approach. Applied Mathematics&Computation, 2012, 219(1):408-415
9 Kumar A, Singh A P. Fuzzy classifier for fault diagnosis in analog electronic circuits. ISA Transactions, 2013, 52(6):816-824
10 Litovski V, Andrejevic M, Zwolinski M. Analogue electronic circuit diagnosis based on ANNs. Microelectronics Reliability, 2006, 46(8):1382-1391
11 Lin H J, Han J R, Zhang X H, Xu J B, Liu Y F. The research on fusion and diagnosis method of multi soft fault of nonlinear analog circuit. International Journal of U&E Service,Science and Technology, 2015, 8(11):191—198
12 Cui J, Wang Y R. A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement, 2011, 44(1):281-289
13 Xie T, He Y G. Fault diagnosis of analog circuit based on high-order cumulants and information fusion. Journal of Electronic Testing, 2014, 30(5):505-514
14 Yuan L F, He Y G, Huang J Y, Sun Y C. A new neuralnetwork-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Transactions on Instrumentation&Measurement, 2010, 59(3):586-595
15 Kowalewski M. Two-center radial basis function network for classification of soft faults in electronic analog circuits. In:Proceedings of the Instrumentation and Measurement Technology Conference. Warsaw, Poland:IEEE, 2007. 1-6
16 Stratigopoulos H G, Mir S, Hora C, Kruseman B. Diagnosis of local spot defects in analog circuits. IEEE Transactions on Instrumentation&Measurement, 2012, 61(10):2701-2712
17 Zhang C L, He Y G, Yuan L F, He W, Xiang S, Li Z G. A novel approach for diagnosis of analog circuit fault by using GMKL-SVM and PSO. Journal of Electronic Testing, 2016,32(5):531-540
18 Yu W X, Sui Y B, Wang J N. The faults diagnostic analysis for analog circuit based on FA-TM-ELM. Journal of Electronic Testing, 2016, 32(4):459-465
19 Han H, Wang H J, Tian S L, Zhang N. A new analog circuit fault diagnosis method based on improved mahalanobis distance. Journal of Electronic Testing, 2013, 29(1):95-102
20 Xu Dan-Lei, Du Lan, Liu Hong-Wei, Hong Ling, Li YanBing. Joint feature selection and classification design based on variational relevance vector machine. Acta Automatica Sinica, 2011, 37(8):932-943(徐丹蕾,杜兰,刘宏伟,洪灵,李彦兵.一种基于变分相关向量机的特征选择和分类结合方法.自动化学报,2011, 37(8):932-943)
21 Tipping M E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research,2001, 1(3):211-244
22 Psorakis I, Damoulas T, Girolami M A. Multiclass relevance vector machines:sparsity and accuracy. IEEE Transactions on Neural Networks, 2010, 21(10):1588-1598
23 Carin L, Dobeck G J. Relevance vector machine feature selection and classification for underwater targets. In:Proceedings of the OCEANS. San Diego, USA:IEEE, 2003.1110-1110
24 Li D F, Hu W C. Feature selection with RVM and its application to prediction modeling. Lecture Notes in Computer Science. Berlin:Springer-Verlag, 2006. 1140-1144
25 Girolami M, Rogers S. Variational Bayesian multinomial probit regression with Gaussian process priors. Neural Computation, 2006, 18(8):1790-1817
26 Tipping M E, Faul A C. Fast marginal likelihood maximisation for sparse bayesian models. In:Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics. Key West, Florida, USA:Miketipping, 2003. 3-6
27 Yang Z R. A fast algorithm for relevance vector machine. In:Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. Burgos, Spain:Springer, 2006. 33-39
2 Ao Y C, Shi Y B, Zhang W, Li Y J. An approximate calculation of ratio of normal variables and its application in analog circuit fault diagnosis. Journal of Electronic Testing Theory&Applications, 2013, 29(4):555-565
3 Tadeusiewicz M, Halgas S. A new approach to multiple soft fault diagnosis of analog BJT and CMOS Circuits. IEEE Transactions on Instrumentation&Measurement, 2015,64(10):2688-2695
4 Tang X F, Xu A Q. Practical analog circuit diagnosis based on fault features with minimum ambiguities. Journal of Electronic Testing, 2016, 32(1):83-95
5 Liu Z B, Liu T M, Han J W, Bu S H, Tang X J, Pecht M.Signal Model-based fault coding for diagnostics and prognostics of analog electronic circuits. IEEE Transactions on Industrial Electronics, 2016, 64(1):605-614
6 Jing Y P, Yuan L F, He Y G. An analog circuit diagnosis method based on Adaptive-Kernel ICA. International Journal of Electronics and Electrical Engineering, 2016, 4(2):116-120
7 Luo H, Wang Y R, Lin H, Jiang Y Y. A new optimal test node selection method for analog circuit. Journal of Electronic Testing, 2012, 28(3):279-290
8 Tan H, Peng M F. Minimization of ambiguity in parametric fault diagnosis of analog circuits:a complex network approach. Applied Mathematics&Computation, 2012, 219(1):408-415
9 Kumar A, Singh A P. Fuzzy classifier for fault diagnosis in analog electronic circuits. ISA Transactions, 2013, 52(6):816-824
10 Litovski V, Andrejevic M, Zwolinski M. Analogue electronic circuit diagnosis based on ANNs. Microelectronics Reliability, 2006, 46(8):1382-1391
11 Lin H J, Han J R, Zhang X H, Xu J B, Liu Y F. The research on fusion and diagnosis method of multi soft fault of nonlinear analog circuit. International Journal of U&E Service,Science and Technology, 2015, 8(11):191—198
12 Cui J, Wang Y R. A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement, 2011, 44(1):281-289
13 Xie T, He Y G. Fault diagnosis of analog circuit based on high-order cumulants and information fusion. Journal of Electronic Testing, 2014, 30(5):505-514
14 Yuan L F, He Y G, Huang J Y, Sun Y C. A new neuralnetwork-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Transactions on Instrumentation&Measurement, 2010, 59(3):586-595
15 Kowalewski M. Two-center radial basis function network for classification of soft faults in electronic analog circuits. In:Proceedings of the Instrumentation and Measurement Technology Conference. Warsaw, Poland:IEEE, 2007. 1-6
16 Stratigopoulos H G, Mir S, Hora C, Kruseman B. Diagnosis of local spot defects in analog circuits. IEEE Transactions on Instrumentation&Measurement, 2012, 61(10):2701-2712
17 Zhang C L, He Y G, Yuan L F, He W, Xiang S, Li Z G. A novel approach for diagnosis of analog circuit fault by using GMKL-SVM and PSO. Journal of Electronic Testing, 2016,32(5):531-540
18 Yu W X, Sui Y B, Wang J N. The faults diagnostic analysis for analog circuit based on FA-TM-ELM. Journal of Electronic Testing, 2016, 32(4):459-465
19 Han H, Wang H J, Tian S L, Zhang N. A new analog circuit fault diagnosis method based on improved mahalanobis distance. Journal of Electronic Testing, 2013, 29(1):95-102
20 Xu Dan-Lei, Du Lan, Liu Hong-Wei, Hong Ling, Li YanBing. Joint feature selection and classification design based on variational relevance vector machine. Acta Automatica Sinica, 2011, 37(8):932-943(徐丹蕾,杜兰,刘宏伟,洪灵,李彦兵.一种基于变分相关向量机的特征选择和分类结合方法.自动化学报,2011, 37(8):932-943)
21 Tipping M E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research,2001, 1(3):211-244
22 Psorakis I, Damoulas T, Girolami M A. Multiclass relevance vector machines:sparsity and accuracy. IEEE Transactions on Neural Networks, 2010, 21(10):1588-1598
23 Carin L, Dobeck G J. Relevance vector machine feature selection and classification for underwater targets. In:Proceedings of the OCEANS. San Diego, USA:IEEE, 2003.1110-1110
24 Li D F, Hu W C. Feature selection with RVM and its application to prediction modeling. Lecture Notes in Computer Science. Berlin:Springer-Verlag, 2006. 1140-1144
25 Girolami M, Rogers S. Variational Bayesian multinomial probit regression with Gaussian process priors. Neural Computation, 2006, 18(8):1790-1817
26 Tipping M E, Faul A C. Fast marginal likelihood maximisation for sparse bayesian models. In:Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics. Key West, Florida, USA:Miketipping, 2003. 3-6
27 Yang Z R. A fast algorithm for relevance vector machine. In:Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. Burgos, Spain:Springer, 2006. 33-39
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