上海交通大学学报(自然版) ›› 2017, Vol. 51 ›› Issue (9): 1104-1110.

• 兵器工业 • 上一篇    下一篇

基于数据驱动的智能电器运行状态监测方法 

 高筱婷,杨东升   

  1.  东北大学 信息科学与工程学院
  • 出版日期:2017-09-20 发布日期:2017-09-20
  • 基金资助:
     

 Research on Operation Condition Monitoring Method of Intellectual Apparatus Based on Data Driven

GAO Xiaoting,YANG Dongsheng    

  1.  College of Information Science and Engineering,Northeastern University
  • Online:2017-09-20 Published:2017-09-20
  • Supported by:
     

摘要:  以满负载条件下频繁操作的交流接触器为研究对象,提出一种基于数据驱动的电器运行状态监测方法.首先,通过试验平台采集交流接触器历史运行数据,得到状态特征参量,并结合小波变换与主成分分析综合评价,对参量数据进行去噪去奇异值等预处理;其次,针对参量数据存在高维、冗余等负面干扰问题,采用核主成分分析方法进行多信息融合,并基于试验数据进行核参数的优选;最后,将融合信息量输入至隐半马尔可夫模型中,实现智能电器运行状态的监测与识别.以CJX28011交流接触器的试验数据为例,验证了所提方法在电器状态监测中的实用性和有效性.

关键词:  , 状态监测, 多信息融合, 核主成分分析, 隐半马尔可夫模型, 智能电器, 小波变换

Abstract:  Alternating current (AC) contactor used frequently under full load is taken as a research object, and a method of apparatus operation condition monitoring based on data driven is proposed in this paper. First, historical operation data of AC contactor are collected by test platform and state characteristic parameters are gained. Combined with comprehensive assessment of wavelet transform and principal component analysis, data preprocessing such as denoising and removing outliers is used. Then, aimed at the disadvantage of highdimensionality and redundancy, kernel principal component analysis (KPCA) is adopted to merge multiinformation and kernel parameters are optimized based on test data. Finally, the fused information is input to hidden semiMarkov model (HSMM), and the operation condition monitoring and recognition of intellectual apparatus are realized. The practicability and validity of the method in apparatus condition monitoring is verified by test data from CJX28011 AC contactor.

Key words: kernel principal component analysis (KPCA), hidden semiMarkov model (HSMM), intellectual apparatus, wavelet transform,  condition monitoring, multiinformation fusion

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