Dynamic-controlled principal component analysis for fault detection and automatic recovery

Zheng, Niannian; Luan, Xiaoli; Shardt, Yuri A.W.; Liu, Fei

doi:10.1016/j.ress.2023.109608

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…A detailed stability analysis for the closed-loop pattern system is given in ref 43. 43 The reference input r k corresponds to the desired pattern set point given by the optimizer in Figure 3.…”

Section: Online Pattern Estimation and Controlmentioning

confidence: 99%

“…Then, at any time k , by considering the latest measurements and using the Bayesian filter, the prior distribution of the pattern can be estimated as where the mean and variance are and the filter gain is determined by After pattern estimation, a tracking controller that consists of pattern feedback and reference inputs is implemented, which is formulated as where K is a prestabilizing matrix for model (eq ), and can be selected as a linear quadratic regulator gain. A detailed stability analysis for the closed-loop pattern system is given in ref 43 . The reference input r k corresponds to the desired pattern set point given by the optimizer in Figure .…”

Section: Application Of Pattern-based Process Optimizationmentioning

confidence: 99%

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Dynamic-Controlled Bayesian Network for Process Pattern Modeling and Optimization

Zheng,

Luan,

Shardt

et al. 2024

Ind. Eng. Chem. Res.

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Capturing the current statistical features of a process and its dynamic evolution is important for controlling and monitoring its overall operational status. In terms of capturing the process dynamics, existing probabilistic latent-variable methods mostly consider autoregressive relationships, and thus, the causality from the control inputs to the pattern, or key hidden variable, remains unmodeled or implicit. To bridge this gap, a model structured by a newly designed dynamic-controlled Bayesian network (DCBN) is proposed in this paper for pattern modeling, especially pattern control and optimization. Significantly, the innovation and advantage of the DCBN lie in explicitly quantifying the impulse response of the pattern under control inputs. As well, the expectation−maximization algorithm is specially designed for learning the DCBN model. Finally, a new framework for pattern-based process control and optimization is presented in which online pattern filtering and control can be implemented. A case study on the combustion process from an industrial boiler illustrates the advantages of the proposed method in that it can capture the controlled dynamics of the process and achieve optimization by tracking the pattern set point or trajectory.

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Section: Online Pattern Estimation and Controlmentioning

confidence: 99%