Fault Diagnosis Method of Analog Circuits Based on Characteristics of the Nonlinear Frequency Spectrum and KPCA
Han Haitao1, 2, Ma Hongguang1, Cao Jianfu2, Zhang Jialiang2
1. The Second Artillery Engineering University Xi’an 710025 China 2. Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an 710049 China
Abstract:For the characteristics that there existed much dimensions and big data volume in extracting fault signatures based on the model of nonlinear output frequency response function (NOFRF), a novel fault diagnosis method, which adopted kernel principal component analysis and multi-class support vector machine(KPCA-MSVM), is proposed to identify different fault states. Firstly, kernel principal component analysis(KPCA)is used to compress data dimension and eliminate nonlinearity among the variables. Secondly, multi-class support vector machine(MSVM)classifier is constructed, and the datum of all kinds of fault states, which were used to train MSVM classifier, are generated by Monte Carlo simulation with PSpice software. The trained MSVM classifier is used to identify different fault state. Via fault diagnosis for sallen-key band pass filter, the results indicate that KPCA-MSVM has good ability to identify and locate parametric faults for analog circuits, and has virtues of fast speed and high precision.
韩海涛, 马红光, 曹建福, 张家良. 基于非线性频谱特征及核主元分析的模拟 电路故障诊断方法[J]. 电工技术学报, 2012, 27(8): 248-254.
Han Haitao, Ma Hongguang, Cao Jianfu, Zhang Jialiang. Fault Diagnosis Method of Analog Circuits Based on Characteristics of the Nonlinear Frequency Spectrum and KPCA. Transactions of China Electrotechnical Society, 2012, 27(8): 248-254.
[1] Milor L S. A tutorial introduction to research on analog and mixed-signal circuit testing[J]. IEEE Transactions on Circuits and System II: Analog and Digitial Signal Processing, 1998, 45:1389-1407. [2] Wang P, Yang S Y. A new diagnosis approach for handling tolerance in analog and mixd-signal circuits by using fuzzy math[J]. IEEE Transaction on Circuits and Systems, 2005, 52(10): 2118-2127. [3] 王军锋, 张维强, 宋国乡. 模拟电路故障诊断的多小波神经网络算法[J]. 电工技术学报, 2006, 21(1): 33-36. Wang Junfeng, Zhang Weiqiang, Song Guoxiang. Fault diagnosis algorithm of analog circuit based on multiwavelet neural network[J]. Transaction of China Electrotechnical Society, 2006, 21(1): 33-36. [4] 彭良玉, 禹旺兵. 基于小波分析和克隆选择算法的模拟电路故障诊断[J]. 电工技术学报, 2007, 22(6): 12-16. Peng liangyu, Yu Wangbing. Fault diagnosis of analog circuit based on wavelet analysis and clonal selection algorithm[J]. Transaction of China Electrotechnical Society, 2007, 22(6): 12-16. [5] Lang Z Q, Billings S A. Output frequency characteristics of nonlinear system[J]. International Journal of Control, 2007, 80(6): 843-855. [6] Lang Z Q, Billings S A. Energy transfer properties of non-linear systems in the frequency domain[J]. International Journal of Control, 2005, 78(5):345-362. [7] Schölkopf B, Smola A, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5):1299-1319. [8] 钟秉翔, 李太福, 汪德彪等. 基于KPCA的功能模拟智能控制系统模型研究[J]. 辽宁工程技术大学学报, 2010, 29(5): 810-813. Zhong Bingxiang, LI Taifu, Wang Debiao, et al. Modeling on intelligent control system with function simulation based on KPCA[J]. Journal of Liaoning Technical University, 2010, 29(5): 810-813. [9] Vapnik V, Lerner A. Pattern recognition using generalized portrait method[J]. Automation and Remote Control, 1963, 24(6):774-780. [10] 邓乃杨, 田英杰著. 数据挖掘中的新方法—支持向量机[M]. 北京: 科学出版社, 2004: 100-122. [11] 孙永奎. 基于支持向量机的模拟电路故障诊断方法研究[D]. 成都:电子科技大学, 2009. [12] Cui J, Wang, Y R. A novel approach of analog circuit fault diagnosis using support vector machines classifier[J]. Measurement, 2011, 44: 281-289. [13] 毛先柏. 基于支持向量机的模拟电路故障诊断研究[D]. 武汉: 华中科技大学, 2009. [14] 蒋少华, 桂卫华, 阳春华等. 基于核主元分析与支持向量机的监控诊断方法及其应用[J]. 中南大学学报, 2009, 40(5): 1323-1328. Jiang Shaohua, Gui Weihua, Yang Chunhua, et al. Method based on kernel principal component analysis and support vector machine and its application[J]. Journal of Central South University, 2009, 40(5): 1323-1328. [15] 许洁, 胡寿松. 基于KPCA和MKL-SVM的非线性过程监控与故障诊断[J]. 仪器仪表学报, 2010, 31(11): 2428-2433. Xu Jie, Hu Shousong. Nonlinear process monitoring and fault diagnosis based on KPCA and MKL-SVM[J]. Chinese Journal of Scientific Instrument, 2010, 31(11): 2428-2433. [16] Kreβel U. Pairwise classification and support vector machine[M]. MA: MIT Press Cambridge, 1999: 255-268. [17] Chang C C, Lin C J. Libsvm: a library for support vector machines[EB/OL]. Software available at 2001, thttp: //www.csie.ntu.edu.tw/~cjlin/libsvm. [18] 韩海涛, 曹建福, 马红光, 等. 非线性输出频域响应函数的自适应辨识算法及其应用[J]. 西安交通大学学报, 2011, 45 (10):77-81. Han Haitao, Cao Jianfu, Ma Hongguang, et al. Adaptive identification algorithm of nonlinear output f requency response functions and its application[J], Journal of Xi’an Jiaotong University, 2011, 10(45): 77-81.