摘要
针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题,提出了一种改进的动态核主元分析故障检测方法,该方法首先利用不可区分度剔除相关程度较小或者不相关变量,减少数据量,然后通过观测值扩展对筛选后的新数据构建增广矩阵,并对矩阵使用核主元分析提取变量数据的非线性空间相关特征,最后通过监测T2和SPE两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明,该方法能对风力发电机故障进行有效监测和诊断,与KPCA方法相比,改进的动态核主元分析方法对微小故障更为敏感。
To solve the problems of low accuracy and large computation in dynamic non-linear detection process of complex industrial system,a fault detection method of improved dynamic kernel principal component analysis(IDKPCA)is proposed.First,the undistinguishable degree is used to eliminate the variables with low correlation degree or no correlation degree,so as to the amount of data is reduced,then the augmented matrix is constructed for the new data after screening by extending the observed value,the nonlinear spatial correlation characteristics of variable data is extracted by KPCA,finally monitoring statistics T2 and SPE are used to diagnose system failure and identify fault variables.Simulation experiment shows that this method can effectively monitor and diagnose the fault of wind turbine,and compared with KPCA method,the improved dynamic kernel principal component analysis method is more sensitive to minor faults.
作者
翟坤
杜文霞
吕锋
辛涛
句希源
ZHAI Kun;DU Wenxia;LYU Feng;XIN Tao;JU Xiyuan(College of Physics and Information Engineering,Hebei Normal University,Shijiazhuang 050024,Hebei,China;College of Career Technology,Hebei Normal University,Shijiazhuang 050024,Hebei,China)
出处
《化工学报》
EI
CAS
CSCD
北大核心
2019年第2期716-722,共7页
CIESC Journal
基金
国家自然科学基金项目(61673160)
河北省自然科学基金项目(F2018205102)
河北省教育厅重点基金项目(ZD2016053)
河北省教育厅青年基金项目(QN2018087)
关键词
主元分析
核函数
故障诊断
动态仿真
算法
principal component analysis
kernel function
fault diagnosis
dynamic simulation
algorithm
