摘要
针对过程数据具有变量非线性耦合、过程尺度多样等特征,提出一种基于多尺度滑动窗口核熵成分分析(MS-MWKECA)自适应工业过程故障检测方法.首先,使用小波变换将过程变量实时地分解成不同的细节尺度和近似尺度,建立基于熵成分分析(KECA)的局部尺度模型,并筛选具有显著特征的尺度用于数据重构;随后,对重构的数据建立基于滑动窗口KECA的在线检测模型,并且在滑动窗口更新数据的过程中,设计一种自适应权重分配的窗口更新融合策略;最后,在Tennessee-Eastman(TE)过程和热轧生产过程(HSMP)中进行仿真测试,通过对比实验验证所提方法的有效性和优越性.
In response to the characteristics of process data such as nonlinear coupling between variables and diverse process scales,this paper proposes an adaptive industrial process fault detection method based on multi-scale moving window kernel entropy component analysis(MS-MWKECA).First,the wavelet transform is used to decompose process variables into different detail scales and approximate scales in real-time.Then,KECA-based local scale models are established,and scales with salient features are selected for data reconstruction.Subsequently,an online detection model based on the moving window KECA is developed for the reconstructed data,and an adaptive weight distribution moving window fusion strategy is designed to update data within the moving window process.Finally,the simulation tests are conducted in the Tennessee-Eastman(TE)process and the hot rolling production process(HSMP),and the effectiveness and superiority of the method proposed are verified through experimental comparison.
作者
李晓瑞
刘建昌
郭青秀
LI Xiao-rui;LIU Jian-chang;GUO Qing-xiu(The College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;The National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,China)
出处
《控制与决策》
北大核心
2025年第12期3587-3596,共10页
Control and Decision
基金
国家自然科学基金项目(62273080)
高等学校学科创新引智计划“111计划”(B16009)。
关键词
多尺度
核熵成分分析
自适应权重分配
滑动窗口
故障检测
multi-scale
ernel entropy component analysis
adaptive weight distribution
move window
fault detection
