Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection

Zhong, Bin; Jing, Wang; Zhou, Jinglin; Wu, Haiyan; Jin, Qibing

doi:10.1021/acs.iecr.5b02559

Cited by 57 publications

(42 citation statements)

References 30 publications

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“…In addition to PCA and its extension methods, other process monitoring methods are also used in different monitoring domains. Zhong proposed [12] a quality-related statistical process monitoring method based on global and local partial least squares (PLS) projection. Zhou proposed [13] an improved PLS algorithm to prove its robustness in process monitoring compared with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Peng

Tian

2020

IEEE Access

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In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

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Section: Introductionmentioning

confidence: 99%

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Peng

Tian

2020

IEEE Access

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“…The performance of MSPM largely relies on how the model describes relationships between variables. MSPM mainly includes principal component analysis (PCA), [8][9][10] dynamic principal component analysis (DPCA), [11][12][13] canonical correlation analysis (CCA), [14] independent component analysis (ICA), [10,15,16] Fisher discriminate analysis (FDA), [17][18][19][20] partial least squares (PLS), [21][22][23][24] and their extension methods. As a typical multivariate statistical method, PCA obtains a small number of unrelated latent variables called principal components by mapping the original data into a low-dimensional space, which retains most of the original variance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced high‐order information extraction for multiphase batch process fault monitoring

2020

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Conventional independent component analysis (ICA) monitoring methods extract the feature information of process data by selecting more important independent components (ICs), which discard a small part of ICs that may contain useful information for faults, leading to unsatisfactory monitoring results. However, when the number of sampling points is greater than that of process variables, the ICA monitoring model does not work well. To address the aforementioned problems, a novel monitoring method, multiphase enhanced high‐order information extraction (MEHOIE), is proposed in this paper. The entire production process was first divided into several steady phases and transition phases by the affinity propagation (AP) phase partitioning method. The enhanced high‐order information extraction (EHOIE) model was then built in each phase for fault monitoring. Finally, the algorithm was applied in the penicillin simulation platform and industrial microbial pharmaceutical process. The flexibility and superiority of this algorithm were verified by comparing it with other conventional methods.

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“…It is widely used in various fields such as fault detection and diagnosis, robot, industrial process control, traffic safety, etc. [6][7][8][9]. However, The method cannot be used directly for tensors.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Feng

Meng

2019

Energies

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Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.

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Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection

Cited by 57 publications

References 30 publications

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Enhanced high‐order information extraction for multiphase batch process fault monitoring

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

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