Application for Fault Diagnosis of Loopers based on Evolutionary KPCA-LSSVM
首发时间:2010-01-29
Abstract:In this paper, an evolutionary hybrid approach is studied for fault diagnosis and it is applied to classify the loopers faults in hot rolling process. The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm (GA), kernel principal component analysis (KPCA) and Least Squares Support Vector Machine (LSSVM), which can obtain better fault recognition rate. Firstly, kernel function concept is introduced, and then GA is used to select the kernel parameter in order to improve the performances of nonlinear feature extraction and fault classification of KPCA-LSSVM method. Secondly, KPCA is used to extract the nonlinear principal features of loopers by adopting the optimal kernel trick to map nonlinearly the data into a feature space and employing the PCA procedure. Thirdly, the nonlinear principal features of loopers are taken as input into a LSSVM to classify the faults of loopers in hot rolling process. The results of contrastive experiments show that the evolutionary KPCA-LSSVM using GA to optimize the kernel parameters can extract fault features associated with the loopers effectively, reduce the computational cost and enhance fault classification properties.
keywords: Fault diagnosis Loopers Kernel Principal Component Analysis Least Squares Support Vector Machine. Genetic Algorithm
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基于进化KPCA-LSSVM的活套故障诊断方法应用研究
摘要:针对钢铁轧制过程故障的特点,我们首先提出一种基于遗传算法、核主元分析与最小二乘支持向量机的混合故障诊断方法。首先,本文引出核函数概念,并使用遗传算法优化核函数参数从而提高KPCA-LSSVM算法的特征提取与分类性能。其次使用KPCA方法将原始空间数据映射到高维空间,并在高维空间进行主元分析,从而降维、去相关性,得到非线性特征向量.最后将降维后的特征主元作为LSSVM输入进行训练和识别,根据LSSVM的输出结果判断工作状态与故障类型. 并且在核参数的选取过程中,本文采取遗传算法,通过最大化数据类间离散度的同时最小化类内离散度的方法,充分利用轧机活套设备正常状态以及故障状态数据的先验知识。并通过对活套的故障验证该方法的实用可行性,仿真结果表明:基于KPCA非线性特征提取和LSSVM分类的故障诊断方法计算速度快,能有效的提取故障特征,识别故障类型.
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基于进化KPCA-LSSVM的活套故障诊断方法应用研究
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