Online process monitoring for complex systems with dynamic weighted principal component analysis

Fei, Zhengshun; Liu, Kangling

doi:10.1016/j.cjche.2016.05.038

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…These techniques provide better performance than the single-variable approach. Among them, PCA [8][9][10][11][12][13][14] and partial least squares 15,16 are the most common techniques.…”

Section: Industrial Process Monitoring Techniquementioning

confidence: 99%

Principal component analysis for process monitoring in distributed system environment

Gao

Tian

Chen

2019

Concurrency and Computation

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In modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee-Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results.

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“…These techniques provide better performance than the single-variable approach. Among them, PCA [8][9][10][11][12][13][14] and partial least squares 15,16 are the most common techniques.…”

Section: Industrial Process Monitoring Techniquementioning

confidence: 99%

Principal component analysis for process monitoring in distributed system environment

Gao

Tian

Chen

2019

Concurrency and Computation

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“…A new weighted PCA was proposed to deal with the situation that useful fault information was submerged, and the change rate of statistic was used to capture the useful information [27]. Through incorporating a weight method into PCA, a dynamic weighted PCA method was proposed where the weights along directions of components with large estimation errors were increased [28].…”

Section: Introductionmentioning

confidence: 99%

Process Monitoring via Key Principal Components and Local Information Based Weights

et al. 2019

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There are two problems in principal component analysis (PCA), which is widely employed in multivariate statistical process monitoring. On one hand, principal components selection according to the variance of the normal training dataset cannot represent the amount of fault information included in the online data. Thus, the useful fault information loss would exist and leads to poor monitoring performance. On the other hand, although the fault information contained in every principal component is different and the principal components are treated equally in traditional PCA-based methods. Then, some useful fault information would be suppressed. In order to reduce the dimension and preserve every original variable information as complete as possible at the same time, this paper selects key principal components using our previously proposed full variable expression method. Moreover, according to the accumulated reachability distance of the online data relative to that of the offline training data, those key principal components with large accumulated reachability distance are emphasized and weighted. Finally, the statistics are constructed to monitor the operation status, and the process monitoring performance of the proposed method is evaluated under an industrial process. INDEX TERMS Statistical analysis, process monitoring, fault detection, principal component analysis, data analysis.

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“…Over the past decades, many process monitoring methods have been developed, − among which principal component analysis (PCA) and partial least-squares (PLS) are most widely used. Thereafter, several extensions to handle various factors, including process dynamics, − nonlinearity, , non-Gaussianity, − are also available. However, since the main task of process monitoring is to maintain the production safety, it cannot satisfy the quest by enterprises for profits any longer.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Multiblock T-PLS Based Operating Performance Assessment for Plant-Wide Processes

Liu

Wang

Gao

et al. 2018

Ind. Eng. Chem. Res.

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With the pursuit of profit, the operating performance assessment of industrial processes has gradually attracted attention and concentration in modern industrial production. However, owing to the complexity of plant-wide processes, many existing operating performance assessment techniques are lacking in interpretation of the assessment result, and the cause identification is also difficult for the nonoptimal operating performance. In this study, a new operating performance assessment method is proposed for plant-wide processes based on hierarchical multiblock total projection to latent structures (T-PLS). The proposed approach divides the process variables into several conceptually meaningful blocks and incorporates block information into high level for operating performance assessment of plant-wide processes. With the new method, the assessment task can be greatly reduced and the interpretability of the assessment models are greatly improved. Once the process operating performance is nonoptimal, the responsible block is first located by the block assessment result, and then the variable contributions are calculated in the corresponding block so as to narrow the search and improve the accuracy of the cause identification result. A case study of the gold hydrometallurgy process evaluates the feasibility and efficiency of the proposed method.

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Online process monitoring for complex systems with dynamic weighted principal component analysis

Cited by 11 publications

References 30 publications

Principal component analysis for process monitoring in distributed system environment

Principal component analysis for process monitoring in distributed system environment

Process Monitoring via Key Principal Components and Local Information Based Weights

Hierarchical Multiblock T-PLS Based Operating Performance Assessment for Plant-Wide Processes

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