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Analog Circuit Fault Classification and Data Reduction Using PCA-ANFIS Technique Aided by K-means Clustering ApproachLAIDANI, I.   , BOUROUBA, N. |
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Author keywords
analog integrated circuits, artificial neural networks, fault diagnosis, fuzzy logic, clustering methods
References keywords
analog(18), fault(17), diagnosis(14), circuits(13), circuit(9), fuzzy(7), electronic(6), method(5), classifier(5), approach(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-11-30
Volume 22, Issue 4, Year 2022, On page(s): 73 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.04009
Web of Science Accession Number: 000920289700009
SCOPUS ID: 85150155847
Abstract
The paper work aims to extract effectively the fault feature information of analog integrated circuits and to improve the performance of a fault classification process. Thus, a fault classification method based on principal component analysis (PCA) and adaptive neuro fuzzy inference system classifier (ANFIS) preprocessed by K-means clustering (KMC) is proposed. To effectively extract and select fault features the traditional signal processing based on sampling technique conducts to different signature parameters. A stimulus pulse signal applied to the circuit under test (CUT) allowed us to get a reference output response. Respecting both specific sampling interval and step, the fault free and the faulty output responses are sampled to create amplitude sample features that will serve the fault classification process. The PCA employed for data reduction has lessened the computational complexity and obtaining the optimal features. Thus more than 75% of data volume decreased without loss of original information. The principal components extracted by this reduction data method have been input into ANFIS aided by KMC to obtain the best fault diagnosis results. The experimental results show a score of 100% diagnostic accuracies for the CUTs. Therefore, our approach has achieved best fault classification precision comparing to other research works. |
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| [1] P. Sun, Z. Yang, Y. Jiang, S. Jia, and X. Peng, "A fault diagnosis method of modular analog circuit based on SVDD and D-S evidence theory," Sensors, vol. 21, no. 20, p. 6889, Oct. 2021. [CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10] [2] V. Manikandan and N. Devarajan, "SBT approach towards analog electronic circuit fault diagnosis," Active and Passive Electronic Components, vol. 2007, pp. 1-12, 2007. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 14] [3] P. Nagaraja and G. Sadashivappa, "Fault diagnosis of circuits using statistical parameters and implementation using classifiers-A survey," in 2016 International Conference on Communication and Signal Processing (ICCSP), Apr. 2016, pp. 2162-2166. [CrossRef] [SCOPUS Times Cited 3] [4] A. D. Spyronasios, M. G. Dimopoulos, and A. A. Hatzopoulos, "Wavelet Analysis for the detection of parametric and catastrophic faults in mixed-signal circuits," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 6, pp. 2025-2038, Jun. 2011. [CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 51] [5] D. Binu and B. S. Kariyappa, "A survey on fault diagnosis of analog circuits: Taxonomy and state of the art," AEU - International Journal of Electronics and Communications, vol. 73, pp. 68-83, Mar. 2017. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 104] [6] A. Kavithaman, V. Manikandan, and N. Devarajan, "Analog circuit fault diagnosis based on bandwidth and fuzzy classifier," in TENCON 2009 - 2009 IEEE Region 10 Conference, Singapore, Nov. 2009, pp. 1-6. [CrossRef] [SCOPUS Times Cited 18] [7] J. W. Bandler and A. E. Salama, "Fault diagnosis of analog circuits," Proc. IEEE, vol. 73, no. 8, pp. 1279-1325, 1985. [CrossRef] [Web of Science Times Cited 271] [SCOPUS Times Cited 366] [8] A. Sai Sarathi Vasan, B. Long, and M. Pecht, "Diagnostics and prognostics method for analog electronic circuits," IEEE Trans. Ind. Electron., vol. 60, no. 11, pp. 5277-5291, Nov. 2013. [CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 190] [9] P. Song, Y. He, and W. Cui, "Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits," Analog Integr Circ Sig Process, vol. 87, no. 3, pp. 427-436, Jun. 2016. [CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 52] [10] A. Kumar and A. P. Singh, "Fuzzy classifier for fault diagnosis in analog electronic circuits," ISA Transactions, vol. 52, no. 6, pp. 816-824, Nov. 2013. [CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 48] [11] A. Arabi, N. Bourouba, A. Belaout, and M. Ayad, "An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits," Integration, vol. 64, pp. 50-59, Jan. 2019. [CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 35] [12] G. Zhao, Y. Liu, Y. Gao, Z. Jiang, and C. Hu, "A new approach for analog circuit fault diagnosis based on extreme learning machine," in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, Oct. 2018, pp. 196-200. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8] [13] G. Cai, L. Wu, and M. Li, "The circuit fault diagnosis method based on spectrum analyses and ELM," in 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), Chengdu, China, Aug. 2021, pp. 475-479. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3] [14] T. Gao, J. Yang, S. Jiang, and C. Yang, "A novel fault diagnostic method for analog circuits using frequency response features," Review of Scientific Instruments, vol. 90, no. 10, p. 104708, Oct. 2019. [CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 23] [15] A. R. Nasser, A. T. Azar, A. J. Humaidi, A. K. Al-Mhdawi, and I. K. Ibraheem, "Intelligent fault detection and identification approach for analog electronic circuits based on fuzzy logic classifier," Electronics, vol. 10, no. 23, p. 2888, Nov. 2021. [CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 29] [16] R. Bharat Ram, V. Prasanna Moorthy, and N. Devarajan, "Fuzzy based time domain analysis approach for fault diagnosis of analog electronic circuits," in Communication and Energy Conservation 2009 International Conference on Control, Automation, Jun. 2009, pp. 1-6 [17] B. Long, M. Li, H. Wang, and S. Tian, "Diagnostics of analog circuits based on LS-SVM using time-domain features," Circuits Syst Signal Process, vol. 32, no. 6, pp. 2683-2706, Dec. 2013. [CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 54] [18] G. Zhao, X. Liu, B. Zhang, Y. Liu, G. Niu, and C. Hu, "A novel approach for analog circuit fault diagnosis based on Deep Belief Network," Measurement, vol. 121, pp. 170-178, Jun. 2018. [CrossRef] [Web of Science Times Cited 95] [SCOPUS Times Cited 109] [19] H. Yang, C. Meng, and C. Wang, "Data-driven feature extraction for analog circuit fault diagnosis using 1-D convolutional neural network," IEEE Access, vol. 8, pp. 18305-18315, 2020. [CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 80] [20] C. Zhang, D. Zha, L. Wang, and N. Mu, "A novel analog circuit soft fault diagnosis method based on convolutional neural network and backward difference," Symmetry, vol. 13, no. 6, p. 1096, Jun. 2021. [CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14] [21] I. T. Jolliffe, Principal component analysis, 2nd ed. 2002 edition. New York: Springer-Verlag New York Inc, pp. 1-9, 2002 [22] M. F. Møller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, vol. 6, no. 4, pp. 525-533, Jan. 1993. [CrossRef] [Web of Science Times Cited 2797] [SCOPUS Times Cited 3393] [23] J.-S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, Jun. 1993. [CrossRef] [Web of Science Times Cited 11112] [SCOPUS Times Cited 14159] [24] N. Talpur, M. N. M. Salleh, and K. Hussain, "An investigation of membership functions on performance of ANFIS for solving classification problems," IOP Conf. Ser.: Mater. Sci. Eng., vol. 226, p. 012103, Aug. 2017. [CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 101] [25] B. Cetisli and A. Barkana, "Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training," Soft Comput, vol. 14, no. 4, pp. 365-378, Feb. 2010. [CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 101] [26] J. Keown, OrCAD Pspice and circuit analysis, 4th ed. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
