摘要
针对核熵成分分析算法(kernel entropy component analysis,KECA)为不同的故障选择相同的核参数影响检测效果的问题,提出了一种基于集成核熵成分分析(ensemble kernel entropy component analysis,EKECA)算法的工业过程故障检测方法。首先,选取一系列具有不同宽度参数的核函数将非线性数据投影到核特征空间,选取Rényi熵值贡献较大的特征值和特征向量,得到转换后的得分矩阵,建立多个KECA子模型;然后,将测试数据投影到各KECA子模型上,计算各KECA子模型的统计量,得到检测结果;最后,将各KECA子模型的检测结果利用Bayesian决策进行概率换算,利用集成学习法计算检测结果统一的统计量,判断其是否超出控制限,并将该算法应用于数值例子和TE过程。仿真结果表明,与传统的EKPCA,KECA等算法相比,所提方法有效提高了故障检测率,降低了误报率。新方法解决了传统KECA算法中不同故障核参数的选择问题,为提高KECA算法在非线性工业过程故障检测中的性能提供了参考。
To solve the problem caused by kernel entropy component analysis(KECA)for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis(EKECA)was proposed.Firstly,a series of kernel functions with different width parameters were selected to project the nonlinear data into the kernel feature space.The eigenvalues and eigenvectors with large contribution to Rényi entropy were selected to obtain the transformed score matrix.The multiple KECAsubmodels were established.Secondly,the test data were projected onto each KECA submodel.The statistics of each KECA submodel were calculated to obtain the detection results.Finally,the detection results of each KECA submodel were turned into probability by Bayesian decision.The unified statistics were calculated by ensemble learning strategy and judged whether it exceeds the control limit.The algorithm was applied to a numerical example and the TE process.The simulation results show that the proposed algorithm can effectively improve the fault detection rate and reduce the false alarm rate compared with traditional EKPCA,KECA and other algorithms.This method solves the problem of selecting kernel parameters for different faults in the traditional KECA algorithm and provides a reference for improving the performance of KECA algorithm in fault detection of nonlinear industrial processes.
作者
郭金玉
赵文君
李元
GUO Jinyu;ZHAO Wenjun;LI Yuan(College of Information Engineering,Shenyang University of Chemical Technology,Shenyang Liaoning,110142,China)
出处
《河北科技大学学报》
CAS
北大核心
2021年第5期481-490,共10页
Journal of Hebei University of Science and Technology
基金
国家自然科学基金(61673279)
辽宁省教育厅一般项目(LJ2019007)。
关键词
自动控制技术其他学科
核熵成分分析
高斯核函数
Bayesian决策
集成学习法
other disciplines of automatic control technology
kernel entropy component analysis
Gaussian kernel function
Bayesian decision
ensemble learning method
