Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine

doi:10.3969/j.issn.1000-1093.2021.02.013

Abstract

Abstract: A multi-layer simplex optimized kernel extreme learning machine (ML-SOKELM) method is proposed based on the testability application framework of simulation-based diagnostic model, which combines the deep learning and kernel-based method. ML-SOKELM method is used to to improve the practicability and accuracy of multi-fault diagnosis of analog circuits. The multi-layer kernel extreme learning machine (ML-KELM) extracts the fault features layer by layer and gets the diagnosed results with original data after primary selection. During the training process, the proposed method is used to optimize the kernel parameters of all layers. The diagnosed results show that, compared with the common deep learning methods, ML-SOKELM methed is less dependent on subjective experience and achieves considerable accuracy while greatly shortening the training time. ML-SOKELM can achieve higher diagnostic accuracy under different ambiguity thresholds in comparison with popular kernel-based methods.

Key words: analogcircuit, kernelextremelearningmachine, deeplearning, faultdiagnosis, ambiguitygroup

CLC Number:

TM133

ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine[J]. Acta Armamentarii, 2021, 42(2): 356-369.

References

［1］ ZHANG C L, HE Y G, YUAN L F, et al. Analog circuit incipi-ent fault diagnosis method using DBN based features extraction［J］. IEEE Access, 2018, 6(5): 23053-23064.
［2］ BINU D, KARIYAPPA B S. RideNN: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits［J］. IEEE Transactions on Instrumentation and Measurement, 2019, 68(1):2-26.
［3］ BINU D, KARIYAPPA B S. A survey on fault diagnosis of analog circuits: Taxonomy and state of the art［J］. AEU-International Journal of Electronics and Communications, 2017, 73:68-83.
［4］ TANG X F, XU A Q, LI R F, et al. Simulation-based diagnostic model for automatic testability analysis of analog circuits［J］. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018,37(7): 1483-1493.
［5］ LUO H， LU W, WANG Y R, et al. A new test point selection method for analog continuous parameter fault［J］. Journal of Electronic Testing: Theory & Applications, 2017, 33(3):339-352.
［6］ TANG X F, XU A Q, NIU S C. KKCV-GA-based method for optimal analog test point selection［J］. IEEE Transactions on Instrumentation and Measurement, 2017, 66(1):24-32.
［7］ KUMAR A， SINGH A P. Fuzzy classifier for fault diagnosis in analog electronic circuits［J］. ISA Transactions, 2013, 52(6):816-824.
［8］ LITOVSKI V, ANDREJEVIAC＇U2 M, ZWOLINSKI M. Analogue electronic circuit diagnosis based on ANNs［J］. Microelectronics Reliability, 2006, 46(8):1382-1391.
［9］袁莉芬, 孙业胜, 何怡刚, 等. 基于小波包优选的模拟电路故障特征提取方法［J］. 电工技术学报, 2018, 33(1):158-165.
YUAN L F, SUN Y S, HE Y G, et al. Fault feature extraction method for analog circuit based on preferred wavelet packet ［J］. Transactions of China Electrotechnical Society, 2018, 33(1): 158- 165. (in Chinese)
［10］ LIU Z B, LIU T M, HAN J W, et al. Signal model-based fault coding for diagnostics and prognostics of analog electronic circuits［J］. IEEE Transactions on Industrial Electronics, 2017, 64(1): 605-614.
［11］ XIE T， HE Y G. Fault diagnosis of analog circuit based on high-order cumulants and information fusion［J］. Journal of Electronic Testing: Theory & Applications, 2014, 30(5):505-514.
［12］ YANG H H, MENG C, WANG C. Data-driven feature extraction for analog circuit fault diagnosis using 1-D convolutional neural network ［J］. IEEE Access, 2020, 8(1): 18305-18315.
［13］ LIU Z B, JIA Z, VONG C M, et al. Capturing high-discriminative fault features for electronics-rich analog system via deep learning［J］. IEEE Transactions on Industrial Informatics, 2017, 13(3):1213-1226.
［14］ XU G W, LIU M, JIANG Z F, et al. Online fault diagnosis method based on transfer convolutional neural networks［J］. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2):509-520.
［15］廖剑, 史贤俊, 周绍磊, 等. 基于局部图嵌入加权罚SVM的模拟电路故障诊断方法［J］. 电工技术学报, 2016, 31(4): 28-35.
LIAO J, SHI X J, ZHOU S L, et al. Analog circuit fault diagnosis based on local graph embedding weighted-penalty SVM［J］. Transactions of China Electrotechnical Society, 2016, 31(4): 28- 35. (in Chinese)
［16］张伟, 刘星, 许爱强, 等. lp范数约束的模拟电路3层多核故障诊断模型［J］. 兵工学报, 2018, 39(7):1352-1363.
ZHANG W, LIU X, XU A Q, et al. Three-layer multiple kernel fault diagnosis model with lp-norm constraint for analog circuit［J］. Acta Armamentarii, 2018, 39(7):1352-1363. (in Chinese)
［17］高明哲,许爱强,唐小峰, 等.基于多核多分类相关向量机的模拟电路故障诊断方法［J］.自动化学报,2019,45(2):434-444.
GAO M Z, XU A Q, TANG X F, et al. Analog circuit diagnostic method based on multi-kernel learning multiclass relevance vector machine［J］. Acta Automatica Sinica, 2019，45(2):434-444. (in Chinese)
［18］ LIYANAARACHCHI L C K, ZHOU H M, HUANG G B. Representational learning with ELMs for big data［J］. IEEE Intelligent Systems,2013,28(6): 31-34.
［19］ KASUN L C， YANG Y， HUANG G B， et al. Dimension reduction with extreme learning machine［J］. IEEE Transactions on Image Processing, 2016, 25(8):3906-3918.
［20］ TANG J X, DENG C W, HUANG G B. Extreme learning machine for multilayer perception［J］. IEEE Transactions on Neural Networks and Learning Systems, 2016,27(4):809-821.
［21］逄珊, 杨欣毅, 张勇, 等. 应用深度核极限学习机的航空发动机部件故障诊断［J］. 推进技术, 2017,38(11): 2613-2621.
PANG S, YANG X Y, ZHANG Y， et al. Application of deep kernel extreme learning machinein aero engine components fault diagnosis［J］. Journal of Propulsion Technology, 2017,38(11): 2613-2621. (in Chinese)
［22］ TANG X F, XU A Q. Practical analog circuit diagnosis based on fault features with minimum ambiguities［J］. Journal of Electro-nic Testing, 2016, 32(1):83-95.
［23］ CHEN X, WANG W, CAO W, et al. Gaussian-kernel-based adaptive critic design using two-phase value iteration［J］. Information Sciences, 2019, 482(6): 139-155.
［24］ LAGARIAS J C, REEDS J A, WRIGHT M H, et al. Convergence properties of the Nelder-Mead simplex method in low dimensions［J］. SIAM Journal on Optimization: A Publication of the Society for Industrial & Applied Mathematics, 2006, 9(1):112-147.
［25］ IOSIFIDIS A, TEFAS A, PITAS I. Graph embedded extreme learning machine［J］. IEEE Transactions on Cybernetics, 2016,46(1):311-324.
［26］ PSORAKIS I， DAMOULAS T, GIROLAMI M A. Multiclass relevance vector machines: sparsity and accuracy［J］. IEEE Tran-sactions on Neural Networks, 2010, 21(10):1588-1598.

[1]	LI Liangfu， CHEN Weidong， GAO Qiang， XU Kailuan， LIU Xuan， HE Xi， QIAN Jun. Deep Learning-based Intelligent Target Recognition Technology for Electro-optical System [J]. Acta Armamentarii, 2022, 43(S1): 162-168.
[2]	LI Liangyu， SU Tiexiong， MA Fukang， WU Xiaojun， XU Chunlong. Fault Diagnosis Method of High-pressure Common Rail System Based on EEMD-SVM [J]. Acta Armamentarii, 2022, 43(5): 992-1001.
[3]	LI Jinqing, LIU Zefei, MAN Zhenlong. Key Generation Method Based on Generative Adversarial Network and Its Application in Low-light-level Image Encryption [J]. Acta Armamentarii, 2022, 43(2): 337-344.
[4]	PU Yunwei， LIU Taotao， GUO Jiang， WU Haixiao. Radar Emitter Signal Recognition Based on Convolutional Neural Network and Coordinate Transformation of AmbiguityFunction Main Ridge [J]. Acta Armamentarii, 2021, 42(8): 1680-1689.
[5]	LI Zhinong, HU Zhifeng, MAO Qinghua, ZHANG Xuhui, TAO Junyong. Variational Nonlinear Chirp Mode Decomposition-synchroextracting Transform Method and Its Application in Fault Diagnosisof Rolling Bearing [J]. Acta Armamentarii, 2021, 42(6): 1324-1330.
[6]	ZHANG Feng, ZHANG Chao, YANG Huamin, WANG Fabin. Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System [J]. Acta Armamentarii, 2021, 42(11): 2418-2423.
[7]	XUE Lingzhi, ZENG Xiangyang, YANG Shuang. Underwater Acoustic Target Recognition Algorithm Based on Generative Adversarial Networks [J]. Acta Armamentarii, 2021, 42(11): 2444-2452.
[8]	YU Yue， WANG Honglun. Deep Learning-based Reentry Predictor-corrector Fault-tolerant Guidance for Hypersonic Vehicles [J]. Acta Armamentarii, 2020, 41(4): 656-669.
[9]	YAN Mengda， YANG Rennong， ZUO Jialiang， HU Dongyuan， YUE Longfei， ZHAO Yu. Real-time Computing of Air-to-air Missile Multiple Capture Zones Based on Deep Learning [J]. Acta Armamentarii, 2020, 41(12): 2466-2477.
[10]	LIANG Jie, REN Jun, LI Lei, QI Hang, ZHOU Hongli. Airport Runway Detection Agorithm Based on Accurate Regression of Typical Geometric Shapes [J]. Acta Armamentarii, 2020, 41(10): 2045-2054.
[11]	YU Yongwei， PENG Xi， DU Liuqing， CHEN Tianhao. Real-time Detection of Parts by Assembly Robot Based on Deep Learning Framework [J]. Acta Armamentarii, 2020, 41(10): 2122-2130.
[12]	LI Jiawei, CUI Tao, LIU Yuhang, SHI Shuainan, SUN Qiang. Construction for Online Observer Based on Exhaust Gas Temperature Dynamic Model [J]. Acta Armamentarii, 2019, 40(8): 1562-1571.
[13]	YAN Liyue, WANG Houjun, LIU Zhen. A Real-time Reliability Prediction Approach for Analog Circuits Based on Noise-assisted Technique and On-site Data Update [J]. Acta Armamentarii, 2019, 40(2): 326-333.
[14]	GUO Weichao, ZHAO Huaishan, LI Cheng, LI Yan, TANG Aofei. Fault Feature Enhancement Method for Rolling Bearing Fault Diagnosis Based on Wavelet Packet Energy Spectrum and Principal Component Analysis [J]. Acta Armamentarii, 2019, 40(11): 2370-2377.
[15]	ZHAI Yuyao, SHI Xianjun, L Jiapeng. Research on Evaluation Method for Testability Index and Modeling of Missile System Based on GSPN [J]. Acta Armamentarii, 2019, 40(10): 2070-2079.