Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Wang, Youqing; Si, Yabin; Huang, Biao; Lou, Zhijiang

doi:10.1002/cjce.23249

Cited by 184 publications

(104 citation statements)

References 110 publications

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“…• The methods proposed in this paper can combine some statistical optimal strategies [47][48][49] to study the parameter estimation algorithms of linear and nonlinear systems [50][51][52][53][54][55][56] and can be extended to other fields. • The simulation results indicate that the F-ML-HLSI algorithm can generate more highly accurate estimates of bilinear systems and has a faster convergence speed than the F-ML-HGI algorithm, the GI algorithm, and the MISG algorithm developed in the work of Meng.…”

Section: Resultsmentioning

confidence: 99%

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

Ding

2019

Adaptive Control & Signal

156

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For a special class of nonlinear systems (ie, bilinear systems) with autoregressive moving average noise, this paper gives the input-output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model and the maximum likelihood principle, a filtering-based maximum likelihood hierarchical gradient iterative algorithm and a filtering-based maximum likelihood hierarchical least squares iterative algorithm are developed for identifying the parameters of bilinear systems with colored noises. The original bilinear systems are divided into three subsystems by using the data filtering technique and the hierarchical identification principle, and they are identified respectively. Compared with the gradient-based iterative algorithm and the multi-innovation stochastic gradient algorithm, the proposed algorithms have higher computational efficiency and parameter estimation accuracy. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.

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Section: Resultsmentioning

confidence: 99%

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

Ding

2019

Adaptive Control & Signal

156

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“…Chebel‐Morello et al proposed the STRASS method based on the k‐way correlation for detecting correlation features. Principal component analysis (PCA) is widely used for feature dimension reduction and process monitoring . The multi‐scale principal component analysis (MSPCA) used by Lau et al and the deep PCA (DePCA) used by Deng et al improved the adverse effects of traditional PCA on feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in industrial chemical processes using optimal probabilistic neural network

Xie

Yang

et al. 2019

Can J Chem Eng

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For fault detection and diagnosis in large‐scale industrial systems, traditional methods have a low classification accuracy, which is an issue. This paper proposes a fault diagnosis method based on the modified cuckoo search algorithm (MCS) to optimize the probabilistic neural network (PNN). The random forest treebagger (RFtb) is used to reduce the data feature and the PNN is trained for fault diagnosis and classification. However, in order to address the problem that the parameters of PNN easily fall into the local optimal value, the MCS algorithm is introduced to globally optimize the hidden layer element smoothing factor (σ) in the PNN. The MCS algorithm uses a parameters update and a better optimization mechanism to achieve excellent global convergence and to effectively improve the fault diagnosis capability of the model. During the testing process using the Tennessee Eastman (TE) process dataset, the performance of the proposed model is assessed by comparing the accuracy and the F1‐score of different methods. Graphs are presented that depict fault classification and diagnostic results for the different models. The results show that the MCS algorithm has a better optimization ability than the traditional optimization algorithm and the proposed combination method can significantly improve the accuracy of the TE process fault diagnosis.

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“…As a result, using the entire batch data to establish a single model for prediction is insufficient to model the process and can inevitably cause prediction accuracy loss. Multimodel approaches divide the data into different groups such that each group represents a single operation phase/mode . Multimodel approaches can be further classified into adaptive model methods and phase‐based model methods.…”

Section: Introductionmentioning

confidence: 99%

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Wang

Gao

et al. 2019

Can J Chem Eng

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Batch processes inherently have multiple operation phases; different phases exhibit different characteristics. Hence, it is reasonable to partition the process into phases and build sub‐phase models for online quality prediction. To this end, a sequential phase partition method based on the information increment is proposed. To address the multiphase behaviours in batch processes, this work utilizes a new information increment index to capture the dynamic characteristics of batch processes along a time direction and divides the process into sub‐phases. Next, phase‐based multiway partial least squares (MPLS) models are built to model within‐phase characteristics and predict the quality‐related variables online. Information increment is able to exploit the process evolution by focusing on the changing variable correlations derived from two adjacent extend time slice. It directly utilizes the available process measurements of successful history batch processes without data transformation or dimensionality reduction. The method is sequential and can overcome the limits of some phase partition methods that may divide the samples with discontinuous time sequence but similar characteristics into the same phase. In addition, the information increment is capable of reflecting the change of the process intuitively with high computation efficiency. Advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli (E. coli) fermentation, respectively.

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Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Cited by 184 publications

References 110 publications

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

Fault diagnosis in industrial chemical processes using optimal probabilistic neural network

Online prediction of quality‐related variables for batch processes using a sequential phase partition method

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