Home > Archive>Volume 31, Issue 8, 2017 >1239-1246. DOI:10.13382/j.jemi.2017.08.012
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Analogue circuit fault diagnosis based on SVM optimized by IPSO
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Affiliation:

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Clc Number:

TN707;TP277

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    Abstract:

    In order to solve the problem that the basic particle swarm (PSO) to optimize the parameter of SVM is easy to fall into local optimum, this paper proposes a modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSOSVM) by introducing the new dynamic inertia weight, global neighborhood search, shrinkage factor and mutation operator of genetic algorithm. The classification result is tested by the common datasets named Iris, Wine and seeds from UCI machine learning repository, the result shows that IPSOSVM classifier is better than GSSVM, AFSASVM, GASVM and PSOSVM classifier in terms of classification accuracy and classification time. The better convergence ability and speed of the IPSOSVM classifier are verified by fault diagnosis of SallenKey bandpass filter, fouropamp biquad highpass filter and nonlinear rectifier circuit.

    Reference
    [1]何星,王宏力,陆敬辉,等. 基于优选小波包和ELM的模拟电路故障诊断[J]. 仪器仪表学报, 2013, 34(11):26142619. HE X, WANG H L, LU J H, et al. Analog circuit fault diagnosis method based on preferred wavelet packet and ELM[J]. Chinese Journal of Scientific Instrument, 2013, 34(11):26142619.
    [2]孙健,王成华. 基于mRMR原则和优化SVM的模拟电路故障诊断[J]. 仪器仪表学报，2013，34(1)：221226. SUN J, WANG CH H. Analog circuit fault diagnosis based on mRMR and optimized SVM [J]. Chinese Journal of Scientific Instrument, 2013, 34(1):221226.
    [3]肖迎群,冯良贵,何怡刚. 基于小波分形和核判别分析的模拟电路故障诊断[J]. 电工技术学报, 2012, 27(8):230238. XIAO Y Q, FENG L G, HE Y G. A fault diagnosis approach of analog circuit using waveletbased fractal analysis and Kernel LDA[J]. Transactions of China Electrotechnical Society, 2012, 27(8):230238.
    [4]丁硕,巫庆辉,常晓恒,等. 基于灰色BP神经网络的实验材料供应预测[J]. 国外电子测量技术, 2016, 35(12):7882. DING SH, WU Q H, CHANG X H, et al. Prediction of laboratory equipment support based on grey relation analysis and BP neural network[J]. Foreign Electronic Measurement Technology, 2016, 35(12):7882.
    [5]YUAN L F, HE Y G, HUANG J Y, et al. A new neuralnetworkbased fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J]. IEEE Transactions on Instrumentation & Measurement, 2010, 59(3):586595.
    [6]王道明,鲁昌华,蒋薇薇,等. 基于粒子群算法的决策树SVM多分类方法研究[J]. 电子测量与仪器学报, 2015, 29(4):611615. WANG D M, LU CH H, JIANG W W, et al. Study on PSObased decisiontree SVM multiclass classification method[J]. Journal of Electronic Measurement and Instrumentation, 2015, 29(4):611615.
    [7]LONG B, XIAN W, LI M, et al. Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance[J]. Neurocomputing, 2014, 133(10):237248.
    [8]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 SH 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.
    [9]张朝龙,何怡刚,袁莉芬,等. 基于核熵成分分析的模拟电路早期故障诊断方法[J]. 仪器仪表学报, 2015, 36(3):675684. ZHANG CH L, HE Y G, YUAN L F, et al. Approach for analog circuit incipient fault diagnosis based on KECA [J]. Chinese Journal of Scientific Instrument, 2015, 36(3):675684.
    [10]宋国明,王厚军,刘红,等. 基于提升小波变换和SVM的模拟电路故障诊断[J]. 电子测量与仪器学报, 2010, 24(1):1722. SONG G M, WANG H J, LIU H, et al. Analog circuit fault diagnosis using lifting wavelet transform and SVM[J]. Journal of Electronic Measurement and Instrument, 2010, 24(1):1722.
    [11]周绍磊,廖剑,史贤俊. RBFSVM的核参数选择方法及其在故障诊断中的应用[J]. 电子测量与仪器学报, 2014, 28(3):240246. ZHOU SH L, LIAO J, SHI X J. Kernel parameter selection of ＲBMSVM and its application in fault diagnosis[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(3):240246.
    [12]张向东,冯胜洋,王长江,等. 基于网格搜索的支持向量机砂土液化预测模型[J]. 应用力学学报, 2011, 28(1):2428. ZHANG X D, FENG SH Y, WANG CH J, et al. Support vector machine model for predicting sand liquefaction based on gridsearch method[J]. Chinese Journal of Applied Mechanics, 2011, 28(1):2428.
    [13]高雷阜,赵世杰,高晶. 人工鱼群算法在SVM参数优化选择中的应用[J]. 计算机工程与应用,2013, 49(23):8690. GAO L F, ZHAO SH J, GAO J. Application of artificial fishswarm algorithm in SVM parameter optimization selection[J]. Computer Engineering and Applications, Computer Engineering and Applications, 2013, 49(23):8690.
    [14]LI J Z, ZHANG Q G, WANG K, et al. Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2016, 23(2):11981206.
    [15]XUE Z H, DU P J, SU H J. Harmonic analysis for hyperspectral image classification integrated with PSO optimized SVM[J]. IEEE Journal of Stars, 2014, 7(6):21312146.
    [16]高立群,李若平,邹德旋. 全局粒子群优化算法[J]. 东北大学学报:自然科学版, 2011, 32(11):15381541. GAO L Q, LI R P, ZOU D X. A global particle swarm optimization algorithm [J]. Journal of Northeastern University:Natural Science, 2011, 32(11):15381541.
    [17]郭明玮,赵宇宙,项俊平,等. 基于支持向量机的目标检测算法综述[J]. 控制与决策, 2014, 29(2):193200. GUO M W, ZHAO Y ZH, XIANG J P, et al. Review of object detection methods based on SVM [J]. Control and Decision, 2014(2):193200.
    [18]翟旭平,杨兵兵,孟田. 基于PCA和混合核函数QPSO_SVM频谱感知算法[J]. 电子测量技术, 2016, 39(9):8790. ZAI X P, YANG B B, MENG T. Spectrum sensing based on PCA and QPSO_SVM with mixed kernel[J]. Electronic Measurement Technology, 2016, 39(9):8790.
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  • Online: September 16,2017
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