CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 716-722.DOI: 10.11949/j.issn.0438-1157.20181411

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Fault detect method based on improved dynamic kernel principal component analysis

Kun ZHAI1(),Wenxia DU2(),Feng LYU2,Tao XIN2,Xiyuan JU2   

  1. 1. College of Physics and Information Engineering, Hebei Normal University, Shijiazhuang 050024, Hebei, China
    2. College of Career Technology, Hebei Normal University, Shijiazhuang 050024, Hebei, China
  • Received:2018-11-26 Revised:2018-12-05 Online:2019-02-05 Published:2019-02-05
  • Contact: Wenxia DU

一种改进的动态核主元分析故障检测方法

翟坤1(),杜文霞2(),吕锋2,辛涛2,句希源2   

  1. 1. 河北师范大学物理科学与信息工程学院,河北 石家庄 050024
    2. 河北师范大学职业技术学院,河北 石家庄 050024
  • 通讯作者: 杜文霞
  • 作者简介:<named-content content-type="corresp-name">翟坤</named-content>(1991—),男,硕士研究生,<email>826540060@qq.com</email>|杜文霞(1973—),女,博士,副教授,<email>dwx20040513@163.com</email>
  • 基金资助:
    国家自然科学基金项目(61673160);河北省自然科学基金项目(F2018205102)

Abstract:

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 T 2 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.

Key words: principal component analysis, kernel function, fault diagnosis, dynamic simulation, algorithm

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T 2SPE 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。

关键词: 主元分析, 核函数, 故障诊断, 动态仿真, 算法

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