Data-Driven Quality Monitoring Techniques for Distributed Parameter Systems With Application to Hot-Rolled Strip Laminar Cooling Process

Dong, Jie; Wang, Qiang; Wang, Mengyuan; Peng, Kaixiang

doi:10.1109/access.2018.2812919

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…In recent years, new ideas and new theories related to big data and machine learning improved rapidly and were widely used in industry. [19][20][21][22] In tube making industry, a huge amount of data generated in each process from heating in furnace to online control cooling process. And the historical data contains many experiences and regular, which has effective and significant guiding for future production.…”

Section: Traditional Similar Tube Algorithmmentioning

confidence: 99%

Online Cooling System and Improved Similar Self-adaptive Strategy for Hot-rolled Seamless Steel Tube

Dong

Zhang

et al. 2021

ISIJ Int.

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Section: Traditional Similar Tube Algorithmmentioning

confidence: 99%

Online Cooling System and Improved Similar Self-adaptive Strategy for Hot-rolled Seamless Steel Tube

Dong

Zhang

et al. 2021

ISIJ Int.

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“…With the mechanization and intellectualization of production and life, complex nonlinear control system is widely used in chemical industry [1], transportation system [2] and other fields [3]. To improve the reliability and availability of complex dynamic system in industrial assembly line, abnormality detection in industrial process described by distributed parameter system (DPS) is of vital importance [4].…”

Section: Introductionmentioning

confidence: 99%

Backstepping-Based Distributed Abnormality Detection for Nolinear Parabolic Distributed Prameter Systems

Chen¹

2022

ENG

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In this paper, we proposed a model-based abnormality detection scheme for a class of nonlinear parabolic distributed parameter systems (DPSs). The proposed methodology consists of the design of an observer and an abnormality detection filter (ADF) based on the backstepping technique and a limited number of in-domain measurements plus one boundary measurement. By taking the difference between the measured and estimated outputs from observer, a residual signal is generated for fault detection. For the detection purpose, the residual is evaluated in a lumped manner and we propose an explicit expression for the time-varying threshold. The convergence properties of the PDE observer and the residual are analyzed by Lyapunov stability theory. Eventually, the proposed abnormality detection scheme is demonstrated on a nonlinear DPS.

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“…Distributed parameter systems (DPSs) widely exist in the industrial processes [1]- [4]. Unlike lumped parameter systems (LPSs), DPSs are often described by one or a set of partial differential equations (PDEs) with corresponding initial and boundary conditions [5].…”

Section: Introductionmentioning

confidence: 99%

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Xu¹,

Tan²,

Fan³

et al. 2021

IEEE Access

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Many industrial thermal processes are large-scale time-varying nonlinear distributed parameter systems (DPSs). To effectively model such systems, dual extreme learning machine based online spatiotemporal modeling with adaptive forgetting factor (AFFD-ELM) is proposed in this paper. This method can recursively update the parameters of the low-order temporal model by using newly arriving data under Karhunen-Loè ve (KL) based space/time separation. In this way, the time-varying dynamics can be tracked real-time very well as output data increases over time. Besides, since the training samples are usually timeliness, adaptive forgetting factor (AFF) is also embedded in this method to improve the online learning effects by adding a reasonable weight to previous data. This online learning strategy makes the process promising for online modeling under continuously samples environment. The proposed method is utilized for online temperature prediction of the curing oven. Simulation results verify the efficiency and viability of the online spatiotemporal model.

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Data-Driven Quality Monitoring Techniques for Distributed Parameter Systems With Application to Hot-Rolled Strip Laminar Cooling Process

Cited by 12 publications

References 33 publications

Online Cooling System and Improved Similar Self-adaptive Strategy for Hot-rolled Seamless Steel Tube

Online Cooling System and Improved Similar Self-adaptive Strategy for Hot-rolled Seamless Steel Tube

Backstepping-Based Distributed Abnormality Detection for Nolinear Parabolic Distributed Prameter Systems

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

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