Block-Wise Parallel Semisupervised Linear Dynamical System for Massive and Inconsecutive Time-Series Data With Application to Soft Sensing

Shao, Weiming; Li, Yougao; Han, Wenxue; Zhao, Dongya

doi:10.1109/tim.2022.3181930

Cited by 14 publications

(2 citation statements)

References 37 publications

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“…With the great improvements in measurement techniques and information technology, a large amount of process data can be expediently collected [ 9 , 10 , 11 , 12 ]. Data-driven methods have attracted increasingly more attention, and are characterized by simple implementation, a broad applicability, and fewer requirements for a model mechanism or process knowledge [ 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study

Wei

Song

2023

Sensors

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Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods.

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Section: Introductionmentioning

confidence: 99%

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study

Wei

Song

2023

Sensors

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“…In addition, semi-supervised learning can also be applied to static probabilistic generative models, such as the semi-supervised PPCA model [36] and the semi-supervised PPLS model [37], so as to capture the variable cross-correlation for soft sensing modeling. Alternatively, to prevent dynamic drift due to data incompleteness, [38] and [39] proposed dynamical soft sensor models for quality variable missing situations where the process global information can be learned from those incomplete samples. Unfortunately, the above semi-supervised methods have not well balanced the relationship between dynamic information and static information when extracting quality-related information.…”

Section: Introductionmentioning

confidence: 99%

A Quality-Related Concurrent Dual-Latent Variable Model and Its Semi-Supervised Soft Sensor Applications

Wang

Ying

2023

IEEE Access

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To provide accurate key variable prediction, data-driven soft sensing techniques have extracted much attention in recent years. Due to different control strategies in industrial processes, it is noticed that the variables in the control loops can be autocorrelated while the others may be static, which needs to be considered simultaneously. In this paper, a quality-related concurrent dual-latent variable (CDLV) model is proposed for soft sensing construction. Two different kinds of latent variables are adopted to learn quality-related dynamic information and quality-related static information respectively. Both quality-related variables are then applied for quality prediction purposes. On this basis, the CDLV model is extended to a semi-supervised form to provide a comprehensive description for the soft sensor design with insufficient quality information. The proposed models are demonstrated by two industrial cases which show superiority over other relative methods in the accuracy of key quality variables prediction.

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A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology

Han,

Shao,

Wei

et al. 2024

Advanced Engineering Informatics

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Block-Wise Parallel Semisupervised Linear Dynamical System for Massive and Inconsecutive Time-Series Data With Application to Soft Sensing

Cited by 14 publications

References 37 publications

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study

A Quality-Related Concurrent Dual-Latent Variable Model and Its Semi-Supervised Soft Sensor Applications

A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology

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