【摘要】 提出一种基于自回归滑动平均(auto-regressive moving average,ARMA)模型双谱分析与离散隐马尔可夫模型(discrete hidden Markov model,DHMM)的电力电子电路故障混合诊断新方法。首先对故障电路采样的数据进行零均值处理;然后采用高阶累积量建立ARMA模型参数并进行双谱分析,通过对双谱矩阵进行矩阵变换提取电路故障信息特征量,再对故障特征数据进行矢量量化;最后应用离散隐马尔可夫模型,设计出电力电子电路的故障分类器。将该方法应用到SS8机车主变流器电路的故障诊断中。结果表明,所提出方法具有较高的正确诊断率和较强的抗噪声能力,在无噪声或加入5%的噪声情况下,正确诊断率均为100%;而当加入10%的噪声时,正确诊断率比DHMM诊断法和GA-BP神经网络诊断法分别高出16.11%和23.79%。该方法在工程中具有实际应用价值。更多还原

【Abstract】 A new method of mixed fault diagnosis was proposed for power electronic circuit based on auto-regressive moving average (ARMA) and discrete hidden markov model (DHMM).Firstly,the fault circuit sampling data was processed by mean normalization method.Afterwards,characteristic quantities of circuit fault information were analyzed and extracted by using ARMA bispectrum analysis,and then it was delt with vector quantization.Finally the discrete hidden Markov models were utilized to design the fault classifier of power electronic circuits.The method was applied to fault diagnosis of the SS8 electric locomotive main converter.The results show that the proposed method has high diagnostic accuracy and good ability to resistance the noise.The diagnosis correct rate of the proposed method is 100% in the case of no noise or adding 5% noise.When adding 10% noise,Its diagnosis correct rate is 16.11% and 23.79% higher respectively than that of DHMM and GA-BP neural network method.The diagnosis method is useful in the engineering.更多还原