Nonlinear Dynamic Process Monitoring Based on Two-Step Dynamic Local Kernel Principal Component Analysis

Fang, Hairong; Tao, Wenhua; Lu, Shan; Lou, Zhijiang; Wang, Yonghui; Xue, Yuanfei

doi:10.3390/pr10050925

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…Lu et al developed two monitoring statistics based on the Wasserstein distance for fault detection [9]. The common methods are principal component analysis (PCA), partial least squares (PLS), independent component analysis (ICA), canonical correlation analysis (CCA), statistical pattern analysis, and kernel based methods [10][11][12][13]. Yin et al compared the multivariate statistical methods for fault detection by using the Tennessee Eastman process (TEP) [14].…”

Section: Fault Detectionmentioning

confidence: 99%

A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

Ren

et al. 2023

Processes

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The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely and reliable response decisions. Because practical industrial processes have multimodal operating conditions, the data distributions of process variables are different. The different data distributions may cause the fault detection model to be invalid. In addition, the fault diagnosis model cannot find the correct root cause variable of system failure by only identifying abnormal variables. There are correlations between the trend states of the process variables. If we do not consider these correlations, this may result in an incorrect fault root cause. Therefore, multimodal industrial process monitoring is a tough issue. In this paper, we propose a three-step framework for multimodal industrial process monitoring. The framework aims for multimodal industrial processes to detect the faulty status timely and then find the correct root variable that causes the failure. We present deep local adaptive network (DLAN), two-stage qualitative trend analysis (TSQTA), and five-state Bayesian network (FSBN) to implement fault detection, identification, and diagnosis step by step. This framework can detect the system failure timely, identify abnormal variables, and find the root cause variable and the fault propagation path. The case studies on the Tennessee Eastman simulation and a practical chlorobenzene production process are provided to verify the effectiveness of the proposed framework in multimodal industrial process monitoring.

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Section: Fault Detectionmentioning

confidence: 99%

A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

Ren

et al. 2023

Processes

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show abstract

“…We can understand the mathematical properties of specific kernel functions to explain the model's behavior [24]. Furthermore, the local kernel function [25] within specific local regions makes it more suitable for addressing local nonlinearity issues. The choice between kernel function and local kernel function depends on the data characteristics and the specific problem.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

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Process monitoring method based on vine copula and transfer learning strategy

Zhang,

2025

Computers & Chemical Engineering

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Nonlinear Dynamic Process Monitoring Based on Two-Step Dynamic Local Kernel Principal Component Analysis

Cited by 9 publications

References 36 publications

A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Process monitoring method based on vine copula and transfer learning strategy

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