Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data

Sadeghian, Anahita; Wu, Ouyang; Huang, Biao

doi:10.1016/j.jprocont.2017.03.012

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…However, the measured process variables are often contaminated because of the practical runtime environment and other operational restrictions. In such cases, traditional LV models used in industrial processes lack in terms of reliability because of the erroneous assumptions of the model and ill-definition of data locality. − …”

Section: Introductionmentioning

confidence: 99%

“…However, the usual selection of priors on the LV and the process noises is Gaussian distribution that is more sensitive to outliers because the log-probability only decays with the symmetric means. To diminish the adverse effects of outliers, the significant deviations in the process noise is statistically compensated by a noise model with a case of Gaussian location mixture. , It is quite likely that the outlying observation represents randomly distributed noise in the data.…”

Section: Introductionmentioning

confidence: 99%

“…To diminish the adverse effects of outliers, the significant deviations in the process noise is statistically compensated by a noise model with a case of Gaussian location mixture. 9,18 It is quite likely that the outlying observation represents randomly distributed noise in the data. An alternative robust configuration involves the use of Student's t distribution for modeling the effect of outliers, owing to its heavy tails.…”

Section: Introductionmentioning

confidence: 99%

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Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables

Luo

Xie

2020

Ind. Eng. Chem. Res.

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A robust and mixture form of Bayesian latent variable regression with structural sparsity (BLVR-SS) is proposed to deal with an inferential sensing problem for the processes with multiple modes and noise components. In particular, a structural sparsity on the joint loading matrix is provided to automatically determine a subset of latent variables. By inducing the column-wise hyperparameter vectors, the dependency of quality variables on process variables can be reliably captured to make the estimation more accurate. In addition, the Student's-t distributions are placed on the latent variables for diminishing the adverse effect of outliers. A mixture form of BLVR-SS is then developed to separate the data into different operating modes. Subsequently, a variational Bayesian expectation maximization algorithm is developed for the parameter learning procedure of the model. The feasibility and efficiency of the developed inferential model is demonstrated through a numerical simulation, the benchmark Tennessee Eastman process, and a pilot scale 1.5 kg/h fluidized catalytic cracking setup.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables

Luo

Xie

2020

Ind. Eng. Chem. Res.

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“…Chretien et al [18] proposed a robust principal component analysis (RPCA) method to build the Low Rank + Sparse models when the used data is corrupted by outliers and applied it to estimate the topology in power grid networks. Sadeghian et al [19] thought that traditional robust principal component analysis (RPCA) algorithms only focused on output outliers, however, both input and output data can make mistakes in developing soft sensors. They built a robust probabilistic predictive model to overcome this problem by appropriate formulation of noise distributions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Zhang

Chai

2019

Energies

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China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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Quantitative Evaluation of Sensor Reconfigurability Based on Data-driven Method

Jiang

2022

Int. J. Control Autom. Syst.

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Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data

Cited by 15 publications

References 28 publications

Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables

Robust Mixture Bayesian Latent Variable Regression with Structural Sparsity and Application to Inferential Sensing of Quality Variables

Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Quantitative Evaluation of Sensor Reconfigurability Based on Data-driven Method

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