基于差分局部保持投影的非线性过程故障检测

Fault Detection of Nonlinear Process Based on Differencial Locality Preserving Projections

为了提高局部保持投影(LPP)算法的非线性过程故障检测性能,提出了一种新的基于差分局部保持投影(differencial locality preserving projections,DLPP)的非线性过程故障检测方法.首先对间歇过程训练数据进行预处理,找到每个样本的最近邻,将该样本与其最近邻进行差分运算,然后运用局部保持投影(LPP)算法进行降维和特征提取,计算样本的SPE统计量,并利用核密度估计(KDE)确定控制限.对于新来的测试样本数据差分处理后,向LPP模型上进行投影,计算新数据的SPE统计量并与控制限比较进行故障检测.最后通过数值例子和半导体过程数据的仿真实验结果验证了该算法的有效性.

To improve the performance of locality preserving projections( LPP) for fault detection of a nonlinear process,we propose a fault detection method of anonlinear process based on differencial LPPs. First,the training data of the batch process are preprocessed. The nearest neighbor of each sample is found. The differencial operation is conducted between the sample and its nearest neighbor. Then,the LPP algorithm is used for dimensionality reduction and feature extraction. The squared prediction error( SPE) statistics of the samples is calculated,and kernel density estimation is used to determine the control limits. The new test sample data are projected onto the LPP model after differencial processing. The SPE statistics is calculated and compared with the control limits for fault detection. Simulation experiment results of numerical examples and semiconductor process data verify the effectiveness of the algorithm.