核主元分析(kernel principal component analysis,KPCA)方法利用核函数把输入空间中的非线性问题转化为特征空间中的线性问题,目前被广泛应用于非线性过程工业的故障检测.采用KPCA算法进行故障检测,核函数中核参数的选择是影响检测结果准确性和可靠性的重要因素.然而,在实际应用中,大多时候是根据经验或采用交叉验证方法进行核参数的选取,因此,通常需要对核参数的取值进行反复调整.这不仅有碍于实现故障检测的自动化、智能化,还难以保证找到的核参数为最优值,从而影响故障检测性能.为此,文中基于二分法思想,提出一种新的KPCA核参数优选方法,并将其用于田纳西—伊斯曼过程(tennessee eastman,TE)故障检测.实验结果表明,该算法能够有效解决KPCA的核参数优选问题,进而确保故障检测结果的准确性和可靠性.
Dichotomy-based optimal selection of KPCA kernel parameters
Kernel principal component analysis(KPCA),by aid of kernel functions,transforms the nonlinear problems in the input space into linear problems in the feature space,which has currently been widely adopted in fault detection by non-linear process industry.The adoption of KPCA algorithm for fault detection finds the selection of kernel parameters in the ker-nel function an important factor affecting the accuracy and reliability of the detection results.Yet in practical applications,the values of kernel parameters are mostly selected,relying priamarily on one's experience or cross-validation method,which fe-quently requires repeated adjustments of the value of the kernel parameters..This not only hinders the automation and intelli-gence of fault detection,but also makes it difficult to ensure that the selected kernel parameters are the optimal values,thus to affect the final performance of fault detection.Therefore,this paper,based on the idea of dichotomy,presents an optimized method of kernel parameters selection in KPCA and applies it to the fault detection of TE process.Experimental results find that the algorithm can effectively solve the kernel parameter optimization problem of KPCA,and ensure the accuracy and relia-bility of fault detection results.
kernel principal component analysiskernel parameters selectionfault detectiontennessee eastman processdichotomy
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