Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Wang, Xuemei; Wu, Ping

doi:10.1021/acsomega.2c01892

Cited by 7 publications

(1 citation statement)

References 52 publications

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“…Kernel mapping is used to solve nonlinear problems in the monitoring and diagnosis process [13,14]. Considering the autocorrelation between process data, Wang XM [15] carried out CVA in the kernel space generated by kernel principal component analysis (KPCA), and established the kernel CVA (KCVA) model to monitor the nonlinear dynamic process, which significantly improved the fault detection performance. To detect the early faults of nonlinear industrial processes, a probability-dependent nonlinear statistical monitoring framework is constructed based on KPCA, which measures the probability distribution changes caused by small displacements [16].…”

Section: Introductionmentioning

confidence: 99%

Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS

Guo,

Sun,

Yang

et al. 2023

Sensors

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Rolling is the main process in steel production. There are some problems in the rolling process, such as insufficient ability of abnormal detection and evaluation, low accuracy of process monitoring, and fault diagnosis. To improve the accuracy of quality-related fault diagnosis, this paper proposes a quality-related process monitoring and diagnosis method for hot-rolled strip based on weighted statistical feature KPLS. Firstly, the process-monitoring and diagnosis model of strip thickness and quality based on the KPLS method is introduced. Then, considering that the KPLS diagnosis method ignores the contribution of process variables to quality, it is easy to misjudge the root cause of quality in the diagnosis process. Based on the rolling mechanism model, the influence weight of strip thickness is constructed. By weighing the statistical data features, a quality diagnosis framework of series structure data fusion is constructed. Finally, the method is applied to the 1580 mm hot-rolling process for industrial verification. The verification results show that the proposed method has higher diagnostic accuracy than PLS, KPLS, and other methods. The results show that the diagnostic model based on weighted statistical feature KPLS has a diagnostic accuracy of more than 96% for strip thickness and quality-related faults.

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Section: Introductionmentioning

confidence: 99%

Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS

Guo,

Sun,

Yang

et al. 2023

Sensors

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A novel fault detection scheme for a nonlinear dynamic process based on generalized non‐negative matrix projection‐maximum mean discrepancy: Application on the DAMADICS benchmark process

Cheng,

Cheng‐Long,

Yun‐Fei

et al. 2023

Can J Chem Eng

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In order to address the issue of minor fault detection in nonlinear dynamic processes, this paper proposes a fault detection method based on generalized non‐negative matrix projection‐maximum mean discrepancy (GNMP‐MMD). Firstly, the GNMP is employed to acquire the residual scores of the samples. Subsequently, a sliding window approach is integrated with MMD for real‐time monitoring of sample status within the residual subspace. In this study, GNMP is utilized to mitigate the impact of non‐Gaussianity in data distribution, while MMD serves to alleviate autocorrelation among samples. A numerical case and experimental data collected from the DAMADICS process are utilized to simulate and validate the proposed method. Compared to traditional principal component analysis (PCA), dynamic principal component analysis (DPCA), dynamic kernel principal component analysis (DKPCA), non‐negative matrix factorization (NMF), GNMP, and MMD, the experiment results clearly illustrate the feasibility of the proposed method.

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A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics

Zhang,

Meng

et al. 2024

Journal of Building Engineering

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Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Cited by 7 publications

References 52 publications

Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS

Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS

A novel fault detection scheme for a nonlinear dynamic process based on generalized non‐negative matrix projection‐maximum mean discrepancy: Application on the DAMADICS benchmark process

A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics

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