Abstract
This paper presents a novel analog circuit fault diagnosis approach using generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm. First, the wavelet coefficients’ energies of impulse responses are generated as features. Then, a diagnosis model is constructed by using GMKL-SVM method based on features. Meanwhile, the PSO algorithm yields parameters for the GMKL-SVM method. Sallen-Key bandpass filter and two-stage four-op-amp biquad lowpass filter fault diagnosis simulations are given to demonstrate the proposed diagnose procedure, and the comparison simulations reveal that the proposed approach has higher diagnosis precision than the referenced methods.
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References
Aminian F, Aminian M (2001) Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor. J Electron Test 17(1):29–36
Aminian F, Aminian M, Collins Jr. HW (2002) Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas 51(3):544–550
Aminian M, Aminian F (2007) A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor. IEEE Trans Instrum Meas 56(5):1546–1554
Arizmendi C, Vellido A, Romero E (2012) Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks. Expert Syst Appl 39(5):5223–5232
Chander A, Chatterjee A, Siarry P (2011) A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst Appl 38(5):4998–5004
Chen Y, Gupta MR, Recht B (2009) Learning kernels from indefinite similarities. Proc. 26th Int. Conf. Mach Learn:145–152
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Cortes C, Mohri M, Rostamizadeh A (2009) L2 regularization for learning kernels. Proc. 25th Conf. Uncertain. Artif Intell:109–116
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proc 6th Int Symp Micromachine Human Sci 39–43
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
Grzechca D, Rutkowski J (2009) Fault diagnosis in analog electronic circuits-the SVM approach. Metrol Meas Syst 16(4):583–598
He Y, Tan Y, Sun Y (2004) Wavelet neural network approach for fault diagnosis of analogue circuits. Proc. Inst. Elect. Eng.—Circuits, Devices Syst 151(4):379–384
Hu M, Chen Y, Kwok JTY (2009) Building sparse multiple-kernel SVM classifiers. IEEE T Neural Networ 20(5):827–839
Jiang C, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44(1):281–289
Liu J, Chen J, Chen S, Ye J (2009) Learning the optimal neighborhood kernel for classification. Proc. Int. Joint Conf. Artif Intell:1144–1149
Long B, Huang J, Tian S (2008) Least squares support vector machine based analog-circuit fault diagnosis using wavelet transform as pre-processor. Proc Int Conf Commun Circuits Syst 1026–1029
Pułka A (2011) Two heuristic algorithms for test point selection in analog circuit diagnoses. Metrol Meas Syst 18(1):115–128
Rakotomamonjy A, Bach F, Canu S, Grandvalet Y (2008) SimpleMKL. J Mach Learn Res 9(1):2491–2521
Spina R, Upadhyaya S (1997) Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process 44(3):188–196
Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43(5):576–586
Tan Y, He Y, Cui C, Qiu G (2008) A novel method for analog fault diagnosis based on neural networks and genetic algorithms. IEEE Trans Instrum Meas 57(11):2631–2639
Tsang IWH, Kwok JTY (2006) Efficient hyperkernel learning using second-order cone programming. IEEE Trans Neural Networ 17(1):48–58
Upendar J, Gupta CP, Singh GK, Ramakrishna G (2010) PSO and ANN-based fault classification for protective relaying. IET Gener Transm Dis 4(10):1197–1212
Vasan ASS, Long B, Pecht M (2013) Diagnostics and prognostics method for analog electronic circuits. IEEE T Ind Electron 60(11):5277–5291
Xiao Y, He Y (2010) A linear ridgelet network approach for fault diagnosis of analog circuit. Sci China Inf Sci 53(11):2251–2264
Xiao Y, He Y (2011) A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA. Neurocomputing 74(7):1102–1115
Yang H, Xu Z, Ye J, King I, Lyu MR (2011) Efficient sparse generalized multiple kernel learning. IEEE Trans Neural Networ 22(3):433–446
Yuan L, He Y, Huang J, Sun Y (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):586–595
Zhang C, He Y, Yuan L, Deng F (2014) A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM. J Electron Test 30(3):343–356
Zhang C, He Y, Zuo L, Wang J, He W (2015) A novel approach to diagnosis of analog circuit incipient faults based on KECA and OAO LSSVM. Metrol Meas Syst 22(2):251–262
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 51577046, 51607004, the State Key Program of National Natural Science Foundation of China under Grant No.51637004, the national key research and development plan “important scientific instruments and equipment development” Grant No.2016YFF0102200, Anhui Provincial Science and Technology Foundation of China under Grant No. 1301022036, Anhui Provincial Natural Science Foundation No.1608085QF157, and Key projects of Anhui Province university outstanding youth talent support program No.gxyqZD2016207.
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Zhang, C., He, Y., Yuan, L. et al. A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO. J Electron Test 32, 531–540 (2016). https://doi.org/10.1007/s10836-016-5616-y
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DOI: https://doi.org/10.1007/s10836-016-5616-y