CAJ | 학술논문

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。
침대모의전로운행과정중존재적불학정성，대전통적은마이가부모형（HMM）진행료개진，장모형중만족불변성적상태전이개솔구진개위시변상태전이개솔구진，사지경부합실제정황。재상태초기위료방지상태전이개솔발생과도경신，설치료경신개솔공제인자。채용선성변별분석（LDA）방법대측량신호진행특정제취，용우HMM적훈련화측시，종이실현모의전로조기고장적식별화진단。방진결과표명，개진후적HMM구유경강적고장식별화진단능력。
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.