一种LMD和近似熵算法的模拟电路特征提取方法
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摘要
针对模拟电路故障信号的非线性和非平稳性,提出了用局域均值分解(Local mean decomposition,LMD)和近似熵算法对模拟电路进行特征提取的方法。利用LMD算法把电路故障信号分解为一系列乘积函数(Product functions,PF),再选取前3个PF分量,求它们的近似熵,作为故障的特征向量。电路发生不同故障时,其输出响应信号的复杂度不同,经LMD分解后的PF分量的复杂度就更不相同,而近似熵可以表征时间序列的复杂度,故用LMD加近似熵可以有效提取故障电路的信息。在对故障进行分类判别时,使用核Fisher判别分析,得出各故障的诊断精度。仿真结果显示,本文的特征提取方法在改善故障电路特征的同时提高了诊断准确度,平均分类精度为97.86%。
The local mean decomposition( LMD) and approximate entropy methods were proposed to extract features of an analog circuit in order to process the non-linear and non-stationary faulty circuit signals. These signals were firstly decomposed into the sum of product functions( PF) according the LMD algorithm. Approximate entropy features,as the feature vectors of the signals,were calculated with the first three product functions. The complexity of output response signals varies from different faulty circuits; the complexity of the PF after LMD are greatly different for the same reason. Meanwhile,the approximate entropy can represent the time complexity so that the features of the faulty circuit can be effectively extracted by combining the LMD with the approximate entropy algorithms. Finally the kernel Fisher discriminant analysis was used to classify faults and detect them accurately.The simulation results show that this feature extraction method can not only extract the features of a faulty circuit but also classify them accurately,and the average classification accuracy is 97.86%.
The local mean decomposition( LMD) and approximate entropy methods were proposed to extract features of an analog circuit in order to process the non-linear and non-stationary faulty circuit signals. These signals were firstly decomposed into the sum of product functions( PF) according the LMD algorithm. Approximate entropy features,as the feature vectors of the signals,were calculated with the first three product functions. The complexity of output response signals varies from different faulty circuits; the complexity of the PF after LMD are greatly different for the same reason. Meanwhile,the approximate entropy can represent the time complexity so that the features of the faulty circuit can be effectively extracted by combining the LMD with the approximate entropy algorithms. Finally the kernel Fisher discriminant analysis was used to classify faults and detect them accurately.The simulation results show that this feature extraction method can not only extract the features of a faulty circuit but also classify them accurately,and the average classification accuracy is 97.86%.
引文
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[2]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):675-684Zhang C 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(in Chinese)
[3]祁涛,张彦斌,邸凯,等.PSO优化LS-SVM在模拟电路故障预测中的应用[J].计算机测量与控制,2016,24(3):4-7Qi T,Zhang Y B,Di K,et al. Analog circuit fault prediction based on LS-SVM optimized by PSO[J].Computer Measurement&Control,2016,24(3):4-7(in Chinese)
[4]陈志凡,单剑锋,宋晓涛,等.基于PSO-LSSVM的模拟电路故障诊断[J].计算机技术与发展,2015,25(5):209-213Chen Z F,Shan J F,Song X T,et al. Analog circuit fault diagnosis based on PSO-LSSVM[J]. Computer Technology and Development,2015,25(5):209-213(in Chinese)
[5] Aminian M,Aminian F. A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor[J]. IEEE Transactions on Instrumentation and Measurement,2007,56(5):1546-1554
[6]唐圣学,蔡红军,李志刚.基于小波与神经网络的模拟电路故障诊断融合方法[J].中南大学学报(自然科学版),2015,46(1):127-134Tang X S,Cai H J,Li Z G. Fault diagnosis fusion method for analog circuits based on wavelet and neural network[J]. Journal of Central South University(Science and Technology),2015,46(1):127-134(in Chinese)
[7]孙萍萍.基于小波包理论的激波信号去噪研究[J].国外电子测量技术,2016,35(7):38-42Sun P P. Denoising of shock signal based on wavelet packet theory[J]. Foreign Electronic Measurement Technology,2016,35(7):38-42(in Chinese)
[8]张超,陈建军.基于LMD近似熵和支持向量机的轴承故障诊断[J].机械科学与技术,2012,31(9):1539-1542,1548Zhang C,Chen J J. Diagnosing faults of bearings with LMD approximate entropy and SVM[J]. Mechanical Science and Technology for Aerospace Engineering,2012,31(9):1539-1542,1548(in Chinese)
[9] Smith J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443-454
[10]邓同龙,刘广璞,牛凯强,等.基于LMD近似熵和PNN的轴承故障诊断[J].煤矿机械,2016,37(1):212-214Deng T L,Liu G P,Niu K Q,et al. Fault diagnosis of bearing based on LMD approximate entropy and PNN[J].Coal Mine Machinery, 2016,37(1):212-214(in Chinese)
[11]钱莉,姚恒,刘牮.基于LMD和SVM算法的模拟电路故障诊断[J].电子科技,2015,28(11):82-85Qian L,Yao H,Liu J. Analog circuits fault diagnosis based on LMD and SVM algorithms[J]. Electronic Science&Technology, 2015,28(11):82-85(in Chinese)
[12]赵荣珍,于昊,徐继刚.基于LMD近似熵与HMM的转子故障诊断方法[J].兰州理工大学学报,2012,38(6):24-29Zhao R Z,Yu H,Xu J G. Fault diagnosis approach for rotor system based on LMD-approximate entropy and HMM[J]. Journal of Lanzhou University of Technology,2012,38(6):24-29(in Chinese)
[13] Liu W Y,Zhou L Q,Hu N N,et al. A novel integral extension LMD method based on integral local waveform matching[J]. Neural Computing and Applications,2016,27(3):761-768
[14]张艳艳.近似熵理论及应用[J].中国医学物理学杂志,2009,26(6):1543-1546Zhang Y Y. The theory of approximate entropy and its application[J]. Chinese Journal of Medical Physics,2009,26(6):1543-1546(in Chinese)
[15] Perronnin F,Dance C. Fisher kernels on visual vocabularies for image categorization[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,MN,USA:IEEE,2007
[16] Mika S,Ratsch G,Weston J,et al. Fisher discriminant analysis with kernels[C]//Proceedings of 1999 IEEE Signal Processing Society Workshop. Neural Networks for Signal Processing IX. Madison, WI, USA:IEEE,1999:41-48