基于改进HMM的模拟电路早期故障识别和诊断
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摘要
针对模拟电路运行过程中存在的不确定性,对传统的隐马尔可夫模型(HMM)进行了改进,将模型中满足不变性的状态转移概率矩阵改为时变状态转移概率矩阵,使之更符合实际情况。在状态初期为了防止状态转移概率发生过度更新,设置了更新概率控制因子。采用线性辨别分析(LDA)方法对测量信号进行特征提取,用于HMM的训练和测试,从而实现模拟电路早期故障的识别和诊断。仿真结果表明,改进后的HMM具有更强的故障识别和诊断能力。
Due to the uncertainties that exist in the running of the analog circuits, the traditional Hidden Markov Model(HMM)approach is improved. The state transition probability matrix of the traditional model is replaced by time-varying one that more satisfies the actual situation. An updating control factor is introduced for avoiding the excess updating of the state transition probability in the initial stage of each state. The Linear Discriminant Analysis(LDA)is used to reduce dimensionality and remove redundancy of the voltage feature vectors, which are for HMM's training and testing in order to achieve recognition and diagnosis of the incipient faults in analog circuit. The experimental results indicate that the improved HMM has better fault recognition capability than the traditional HMM.
Due to the uncertainties that exist in the running of the analog circuits, the traditional Hidden Markov Model(HMM)approach is improved. The state transition probability matrix of the traditional model is replaced by time-varying one that more satisfies the actual situation. An updating control factor is introduced for avoiding the excess updating of the state transition probability in the initial stage of each state. The Linear Discriminant Analysis(LDA)is used to reduce dimensionality and remove redundancy of the voltage feature vectors, which are for HMM's training and testing in order to achieve recognition and diagnosis of the incipient faults in analog circuit. The experimental results indicate that the improved HMM has better fault recognition capability than the traditional HMM.
引文
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[2]El-Gamal M A,Abdulghafour M.Fault isolation in analog circuits using a fuzzy inference system[J].Computers and Electrical Engineering,2003,29:213-229.
[3]Mohsen A A K,Abu EI-Yazeed M F.Selection of input stimulus for fault diagnosis of analog circuits using ARMA model[J].International Journal of Electronics and Communications,2004,58(3):212-217.
[4]马超,陈西宏,姚懿玲,等.模拟电路故障诊断的双重扰动支持向量机集成方法[J].微电子学与计算机,2011,28(3):17-22.
[5]许丽佳,王厚军,龙兵.一种状态监测与健康评估方法及其在模拟电路中的应用[J].计算机辅助设计与图形学学报,2008,20(12):1550-1556.
[6]许丽佳,黄建国,王厚军.基于HMM的模拟电路故障诊断方法[J].计算机辅助设计与图形学学报,2010,22(7):1215-1222.
[7]Rabiner L R,Huang B H.An introduction to Hidden Markov Models[J].IEEE Signal Processing Magazine,1986,3(1):4-16.
[8]Lee J M,Kim S J,Hwang Y,et al.Diagnosis of mechanical fault signals using continuous Hidden Markov Model[J].Journal of Sound and Vibration,2004,27(6):1065-1080.
[9]Camci F,Chinnam R B.Hierarchical HMMs for autonomous diagnostics and prognostics[C]//International Joint Conference on Neural Networks,2006:2445-2452.
[10]夏震宇,杨波.基于改进HSMM的设备故障预测方法研究[J].现代制造工程,2011(8):118-122.