Latent variable models in the era of industrial big data: Extension and beyond

Kong, Xiangyin; Jiang, Xiaoyu; Zhang, Bingxin; Yuan, Jing; Ge, Zhiqiang

doi:10.1016/j.arcontrol.2022.09.005

Cited by 52 publications

(20 citation statements)

References 181 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are considered. As presented in the work of Zhou et al, [11] IDV (1,2,5,6,7,8,12,13) are defined as quality-related faults, and IDV (3,4,9,11,15) are qualityunrelated faults.…”

Section: Tennessee Eastman Processmentioning

confidence: 99%

“…Thus, data-driven process monitoring methods have received considerable attention in recent years. [6][7][8][9] Among the classical multivariate statistical process monitoring (MSPM) methods, the partial least squares (PLS) model is commonly used to develop quality-related process monitoring methods. [10] In the standard PLS, the covariance between process variables and final quality variables is maximized, resulting in the space of process variables being decomposed into quality-unrelated and quality-related subspaces.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, data‐driven process monitoring methods have received considerable attention in recent years. [ 6–9 ]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

Kernel partial least squares (KPLS) is widely employed to address the issue of nonlinearity inherent in complex industrial processes. However, KPLS can only extract shallow features from process measurements. This paper proposes a new quality‐related process monitoring method via integrating stacked sparse autoencoder (SSAE) with KPLS (SSAE‐KPLS). First, an SSAE model is employed to exploit the nonlinearity within process variables. Through SSAE, hierarchical features are learned to extract latent representations of process variables from multiple sparse autoencoder (SAE) layers. Second, the learned hierarchical features from SSAE are used as input, and the final quality variables are used as output. A KPLS model is then built to exploit the nonlinear relationship between the hierarchical features extracted from process variables and the final product quality for process monitoring. Third, Hotelling's and statistics are employed to detect the quality‐unrelated and quality‐related faults, respectively. Finally, experiments on a numerical example and the commonly used industrial benchmark of the Tennessee Eastman process (TEP) are conducted to illustrate the efficacy and merits of the proposed SSAE‐KPLS based quality‐related process monitoring method by comparing it with other related methods.

show abstract

“…are considered. As presented in the work of Zhou et al, [11] IDV (1,2,5,6,7,8,12,13) are defined as quality-related faults, and IDV (3,4,9,11,15) are qualityunrelated faults.…”

Section: Tennessee Eastman Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE) have been widely used in industrial processes as typical deep learning. In particular, these two methods are also known as Deep Latent Variable Models (DLVMs) . For example, Liu et al proposed a DBN to extract nonlinear features to predict the outlet oxygen content of combustion systems online.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, these two methods are also known as Deep Latent Variable Models (DLVMs). 23 For example, Liu et al 24 proposed a DBN to extract nonlinear features to predict the outlet oxygen content of combustion systems online. Wang et al 25 combined SAE with support vector regression (SVR) to propose a deep network soft sensor model to estimate rotor deformation of air preheaters in boilers of thermal power plants.…”

Section: Introductionmentioning

confidence: 99%

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application

et al. 2023

View full text Add to dashboard Cite

Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features. In this SA-BiLSTM model, an attention mechanism is introduced to calculate the correlation between hidden features in each time step, thus avoiding the loss of important information. Furthermore, this approach combines historical quality information and a moving window through a supervised strategy of quality variables. Such manipulation not only extracts and exploits nonlinear dynamic latent information from the process and quality variables but also enhances the model's learning efficiency and overall prediction performance. Finally, two real industrial examples demonstrate the superiority of the proposed method compared to conventional methods.

show abstract

Artificial intelligence for dementia prevention

Newby,

Orgeta,

Marshall

et al. 2023

Alzheimer's & Dementia

View full text Add to dashboard Cite

INTRODUCTIONA wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.METHODSML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.RESULTSRisk‐profiling tools may help identify high‐risk populations for clinical trials; however, their performance needs improvement. New risk‐profiling and trial‐recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug‐repurposing efforts and prioritization of disease‐modifying therapeutics.DISCUSSIONML is not yet widely used but has considerable potential to enhance precision in dementia prevention.Highlights Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk‐profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk‐management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

show abstract

Latent variable models in the era of industrial big data: Extension and beyond

Cited by 52 publications

References 181 publications

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application

Artificial intelligence for dementia prevention

Contact Info

Product

Resources

About