Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model

被引：42

作者：

Huang Darong ^{[1
]}

Ke Lanyan ^{[1
]}

Chu Xiaoyan ^{[1
]}

Zhao Ling ^{[1
]}

Mi Bo ^{[1
]}

机构：

[1] Chongqing Jiaotong Univ, Inst Informat Sci & Engn, Chongqing 400074, Peoples R China

来源：

MICROELECTRONICS RELIABILITY | 2018年 / 82卷

关键词：

Predictive neural network; Intuitionistic fuzzy sets (IFS); Hidden Markov Model (HMM); Fault diagnosis; Motor drive system; CLASSIFICATION;

D O I：

10.1016/j.microrel.2018.01.017

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Fault diagnosis for the motor drive system of urban rail transit could reduce the hidden danger and avoid the disaster events as far as possible. In this paper, an improved Hidden Markov Model (HMM) algorithm is proposed for fault diagnosis of motors equipment for urban rail transit. In this approach, the initial value for observation matrix B in HMM is selected based on the predictive neural network and intuitionistic fuzzy sets. Firstly, by predictive neural network the observation probability matrix B is described qualitatively based on its mathematical explanation. Then, the quartering approach is introduced to define the rules between non-membership degree and observation probability matrix B, which obtains the matrix B quantitatively. Next, the selection algorithm for matrix B is given. Finally, the experiments about the motor drive system fault diagnosis of the urban rail transit are made to prove the feasibility for the proposed algorithm.

引用

页码：179 / 189

页数：11

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