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.
出处
《计算机工程与应用》
CSCD
2014年第3期261-264,共4页
Computer Engineering and Applications
关键词
隐马尔可夫模型
故障诊断
线性辨别分析
模拟电路
Hidden Markov Model（HMM）
fault diagnosis
Linear Discriminant Analysis（LDA）
analog circuit
