周卫庆,司风琪,徐治皋,黄葆华,仇晓智.基于KPCA残差方向梯度的故障检测方法及应用[J].仪器仪表学报,2017,(10):2518-2524
基于KPCA残差方向梯度的故障检测方法及应用
Fault detection method based on KPCA residual direction gradient and its application
  
DOI:
中文关键词:  核主元分析  故障检测  方向梯度  故障变量辨识  残差污染
英文关键词:kernel principal component analysis (KPCA)  fault detection  direction gradient  fault variable identification  residual contamination
基金项目:国家自然科学基金(51176030)、南京工程学院引进人才科研启动基金(YKJ201445)项目资助
作者单位
周卫庆 南京工程学院电力仿真与控制工程中心南京211167 
司风琪 东南大学能源热转换及其过程测控教育部重点实验室南京210096 
徐治皋 东南大学能源热转换及其过程测控教育部重点实验室南京210096 
黄葆华 华北电力科学研究院有限责任公司北京100045 
仇晓智 华北电力科学研究院有限责任公司北京100045 
AuthorInstitution
Zhou Weiqing Electric Power Simulation and Control Engineering Center, Nanjing Institute of Technology, Nanjing 211167, China 
Si Fengqi Key Laboratory of Energy Thermal Conversation and Control of Ministry of Education, Southeast University,Nanjing 210096, China 
Xu Zhigao Key Laboratory of Energy Thermal Conversation and Control of Ministry of Education, Southeast University,Nanjing 210096, China 
Huang Baohua North China Electric Power Research Institute Co. Ltd, Beijing 100045, China 
Qiu Xiaozhi North China Electric Power Research Institute Co. Ltd, Beijing 100045, China 
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中文摘要:
      针对核主元分析(KPCA)在应用过程中非线性映射不存在原像、故障变量无法辨识、工程应用困难等问题,提出了一种改进的KPCA残差方向梯度故障检测方法。利用主元统计量和残差统计量的偏微分之间存在着相关性这一性质,对与主元统计量相关的格拉姆矩阵偏微分中间计算过程进行优化,提出一种新的KPCA残差方向梯度算法,在此基础上结合统计量形成系统故障检测的新方法。非线性系统仿真表明,改进的KPCA残差方向梯度法不仅具有较优的故障变量辨识能力,还极大地减小了计算量,缩短了计算时间。大型热力系统的应用进一步表明,无论对于单故障和多故障的情况,方法均具有较好的故障检测能力,并且不存在残差污染,易于工程实现。
英文摘要:
      Aiming at the fact that there is no preimage in nonlinear mapping and fault variable cannot be identified which result in that it is difficult for engineering application of kernel principle analysis, an improved KPCA residual direction gradient algorithm is proposed to overcome the above drawbacks in this paper. By use of the correlation between the partial differential of principle statistic and residual statistic, the gram matrix partial differential intermediate computation process is simplified and the KPCA residual direction gradient index is obtained, combined with residual statistic a new fault detection method is proposed. Nonlinear system simulation computation shows that improved KPCA residual direction gradient method has excellent capability of fault variable identification while computational complexity is greatly decreased and the calculation time is shortened. Furthermore, large scale thermodynamic system application shows that the proposed method has better capability in fault detection whenever in case of single fault or multiple faults and there is no residual contamination while it is very suitable for engineering realization.
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