Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis
Sensorless Drive Diagnosis can be used to assess the process data without the need for additional costintensive sensor technology,and you can understand the synchronous motor and connecting parts of the damaged state.Considering the number of features involved in the process data,it is necessary to perform feature selection and reduce the data dimension in the process of fault detection.In this paper,the MOEA/D algorithm based on multiobjective optimization is used to obtain the weight vector of all the features in the original data set.It is more suitable to classify or make decisions based on these features.In order to ensure the fastness and convenience sensorless drive diagnosis,in this paper,the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive,which makes the fault detection and elimination process more targeted.
Qing Zhou Ling He PengFei Lu
Wuhan University of Technology,School of Automation,122 Luoshi Road,Wuhan,China Wuhan University of Technology,School of Management,205 Xiongchu Road,Wuhan,China
国际会议
珠海
英文
1-4
2017-09-23（万方平台首次上网日期，不代表论文的发表时间）