基于DW-ICA-SVM的工业过程故障检测算法

为了有效提高支持向量机(SVM)算法的故障检测和监视性能,提出一种新的基于DW-ICA-SVM的工业过程故障检测算法。首先,对训练数据进行标准化,运用独立元分析(ICA)获取数据的独立元矩阵,提取隐藏的非高斯信息。其次,运用杜宾-瓦特森(Durbin-Watson, DW)准则计算独立元(ICs)的DW值,通过DW方法有效提取重要噪声信息,选取重要的ICs。再次,将包含重要信息的ICs作为SVM模型的输入,获得判别分类函数,将测试数据的ICs输入该模型,对其进行故障检测和监视。最后,将新算法运用于非线性数值例子和田纳西-伊斯曼工业过程,并与PCA,LPP,ICA,SVM和ICA-SVM方法进行比较。结果表明,所提方法降低了样本间的自相关性,有效提高了故障检测率。因此,新算法在一定程度上加强了对隐藏非高斯信息的提取与识别,为提高SVM算法在工业过程故障检测中的应用性能提供了参考。

Fault detection algorithm of industrial process based on DW-ICA-SVM

In order to effectively improve the fault detection and monitoring performance of support vector machine (SVM) algorithm,a new fault detection algorithm of industrial process based on DW-ICA-SVM was proposed.Firstly,the training data was normalized.The independent component analysis (ICA) was used to obtain the independent component matrix of the data and extract the hidden non-Gaussian information.Then the Durbin-Watson (DW) criterion was used to calculate the DW values of the independent components (ICs).The DW method was used to effectively extract important noise information and select the important ICs.The ICs containing important information were used as the input of the SVM model to obtain the discriminant classification function.The ICs of test data were input to the model for fault detection and monitoring.Finally,the method was applied to the nonlinear numerical example and the Tennessee-Eastman industrial process,and compared with PCA,LPP,ICA,SVM and ICA-SVM methods.The results show that the proposed method reduces the autocorrelation among samples and effectively improves the fault detection rate.The proposed method strengthens the extraction and recognition of hidden non-Gaussian information to a certain extent,and provides a reference for improving the performance of SVM algorithm in fault detection of industrial process.