冯成成
（1）. 标题：Industrial process fault detection based on locally linear embedded latent mapping
摘要：The aim of this study is fault detection in industrial processing with nonlinear or high dimensions. We attempt to integrate the LM algorithm into the local linear embedding algorithm, and propose a novel local linear embedding latent mapping (LLELM) algorithm for fault detection. The LLELM is designed to improve the performance of process monitoring. Our objective is to preserve the global characteristics of the original sample space in the projected lowdimensional space, while preserving the local neighborhood structure of the sample. This research comprises three aspects. First, LLELM overcomes the shortcomings of LM and can effffectively deal with the nonlinear problem. Second, LLELM makes up for the shortcomings of LLE, and can retain the main information from the data after dimensional reduction. Third, LLELM can improve the fault detection rate and can reduce the false alarm rate compared with LM, which is proven by simulation of the TE process.
（2）.标题：A nonlinear method for monitoring industrial process
摘要：Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.
2. 杨冬昇
（1）.标题：Local component based PCA model for Multimode Process Monitoring.
摘要：For plant-wide processes with multiple operating conditions, the multimode feature imposes some challenges to conventional monitoring techniques. Hence, to solve this problem, this paper provides a novel local component based principal component analysis (LCPCA) approach for monitoring the status of a multimode process. In LCPCA, the process prior knowledge of mode division is not required and it purely based on the process data. Firstly, LCPCA divides the processes data into multiple local components using finite Gaussian component (FGMM). Then, calculating the posterior probability is applied to determine each sample belonging to which local component. After that, the local component information (such as mean and standard deviation) is used to standardize each sample of local component. Finally, the standardized samples of each local component are combined to train PCA monitoring model. Based on the PCA monitoring model, two monitoring statistics T2 and SPE are used for monitoring multimode processes. Through a numerical example and the Tennessee Eastman (TE) process, the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.
(2). 标题：层次变分高斯混合模型与主多项式分析的故障检测策略
摘要：针对多模态工业过程中模态数量难以确定问题，提出一种层次变分高斯混合模型(Hierarchical Variational Gaussian mixture model, HVGMM)。在此基础上，使用主多项式分析(Principal polynomial analysis, PPA)用于多模态非线性过程故障检测。首先，变分贝叶斯高斯混合模型(Variational Bayesian Gaussian mixture model, VBGMM)作为初始模型用于分解过程数据得到工作模态的初始数量，将过程按初始数量分解为多个子块；其次，应用包含多个局部模型的VBGMM将各子块分解为附属子块，并利用附属子块的均值、精度等信息对VBGMM进行重构；然后，将重构后的VBGMM作为初始模型再次用于分解原始过程数据，重复上述步骤直至重构VBGMM无法分解各子块时停止；最后，分别在各附属子块中建立局部PPA模型，并在每个局部模型中计算T2和SPE统计量进行故障检测。将该方法应用于数值例子和田纳西伊斯曼(Tennessee Eastman, TE)化工过程，并将仿真结果与主元分析(Principal Component Analysis, PCA)、PPA进行对比，验证了所提出方法的有效性。
