Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

Yuan, Xiaofeng; Huang, Biao; Wang, Yalin; Yang, Chunhua; Gui, Weihua

doi:10.1109/tii.2018.2809730

Cited by 471 publications

(171 citation statements)

References 26 publications

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“…The batch process is an important part of modern industry, and the safety monitoring of the batch process is very important and meaningful . Theoretical research based on data‐driven modeling methods, including soft sensor modeling and process monitoring, has made significant progress and has become increasingly intelligent with industrial processes . For the batch process, multiway principal component analysis (MPCA) is one of the most basic and the most extensively used monitoring methods .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Theoretical research based on data-driven modeling methods, including soft sensor modeling and process monitoring, has made significant progress and has become increasingly intelligent with industrial processes. [4][5][6][7][8][9][10][11][12] For the batch process, multiway principal component analysis (MPCA) is one of the most basic and the most extensively used monitoring methods. 13,14 The requirement for PCA-based monitoring methods is that the data must be subject to Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

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Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Yan

Jiang

et al. 2019

Journal of Chemometrics

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Batch process data are time‐varying dynamic and non‐Gaussian distributed. In addition, for multivariate statistical process monitoring, their variability can be overwhelmed when considering local variability behavior. To address the abovementioned issues, an improved batch process monitoring approach is presented that integrates just‐in‐time learning and multiple subspace support vector data description (JITL‐MSSVDD). A new multiple subspace segmentation method is proposed that classifies a contribution array that is calculated on the mixing matrix of independent component analysis (ICA). Offline, the variable subspace segmentation rule can be obtained from the proposed method. The subspace monitoring models can reduce the risk of the variability being overwhelmed. Online, local modeling samples are collected through JITL, which can reduce the impact of the time‐varying dynamic on modeling accuracy. Then, in accordance with the variable subspace segmentation rule, which is obtained offline, MSSVDD models are constructed to solve non‐Gaussian problems. The advantage of the proposed JITL‐MSSVDD is demonstrated through the standard test model fed‐batch penicillin fermentation process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Yan

Jiang

et al. 2019

Journal of Chemometrics

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“…To overcome limitations of the epidemiological model approach and assist public health planning and policy making, we developed the modified auto-encoder (MAE), an artificial intelligence (AI) based method for real time forecasting of the new and cumulative confirmed cases of Covid-19 under various interventions in more than 100 countries across the world (10,11). The MAE can model interventions, while still using real data for evaluation of interventions.…”

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confidence: 99%

Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19

et al. 2020

Preprint

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medRxiv preprint As COVID-19 evolves rapidly, the issues the governments of affected countries facing are whether and when to take public health interventions and what levels of strictness of these interventions should be, as well as when the COVID-19 spread reaches the stopping point after interventions are taken. To help governments with policy-making, we developed modified autoencoders (MAE) method to forecast spread trajectory of Covid-19 of countries affected, under different levels and timing of intervention strategies. Our analysis showed public health interventions should be executed as soon as possible. Delaying intervention 4 weeks after March 8, 2020 would cause the maximum number of cumulative cases of death increase from 7,174 to 133,608 and the ending points of the epidemic postponed from Jun 25 to Aug 22.All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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“…Meel et al 29,30 developed a complete dynamic risk assessment methodology for process facilities, termed as dynamic failure assessment, which aimed at estimating the dynamic probabilities of accident sequences. Yuan et al 35,36 proposed a deep learningbased variable-wise weighted stacked autoencoder for hierarchical output-related feature representation layer by layer and developed a supervised long short-term memory network to learn quality-relevant hidden dynamics for soft sensor application. Recently, deep learning techniques have been developed and gained great success in industrial processes.…”

mentioning

confidence: 99%

“…Recently, deep learning techniques have been developed and gained great success in industrial processes. Yuan et al 35,36 proposed a deep learningbased variable-wise weighted stacked autoencoder for hierarchical output-related feature representation layer by layer and developed a supervised long short-term memory network to learn quality-relevant hidden dynamics for soft sensor application. Shang et al 37 exploited deep belief network to build soft sensor for a crude distillation unit and discussed the unique advantages of deep learning for industrial processes.…”

mentioning

confidence: 99%

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Chang

Zhang

et al. 2019

Energy Science & Engineering

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The marine natural gas hydrate is considered to have huge resource potential in the South China Sea, and a sixty‐day production test has been successfully conducted in 2017. As the crucial and most vulnerable connection of floating platform and subsea wellhead, the risers are of paramount importance in safe production test of the hydrate. Due to harsh environment and the possible drive off and drift off events induced by failure of dynamic positioning system of the platform, the unplanned emergency disconnect of risers is an integral preventive measure to avoid accidents and secure the well. Once the disconnect occurs, the huge potential energy stored in the riser column will release and consequently cause violent axial recoil, which will cause serious consequences if the recoil control fails. This paper presented a quantitative risk assessment approach on the basis of dynamic Bayesian networks to dynamically predict the risk of riser recoil control failure during production test of hydrate. By using the proposed method, the dynamic failure risk of the riser recoil control was figured out, and the most contributory factors were identified. The practical application of the proposed methodology was demonstrated through a case study. Results indicated that the probability of riser recoil control failure can be significantly decreased within one year through equipment repair, and the failure and repair rates of shutoff valves and main air isolation valves are the most contributory factors. Some corresponding safety control measures were proposed to further mitigate the risk of riser recoil control failure during the production test of hydrate.

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Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

Cited by 471 publications

References 26 publications

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

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