Quality-Related and Process-Related Fault Monitoring With Online Monitoring Dynamic Concurrent PLS

Kong, Xiangyu; Cao, Zehao; An, Qiusheng; Gao, Yingbin; Du, Binbin

doi:10.1109/access.2018.2872790

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Prior knowledge can provide great help for fault detection. We can classify those faults based on the description of each fault (Kong et al, 2018; Li et al, 2019; Wang and Jiao, 2017). Faults 1, 2, 5, 6, 7, 8, 10, 12, 13, and 21 are quality-relevant, and T L 2 | | T NL 2 is used to monitor them because we cannot further obtain the linear or nonlinear information.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network

Yang

Yan

et al. 2023

Transactions of the Institute of Measurement and Control

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Fluid catalytic cracking (FCC) is an important process in petroleum processing. Effective monitoring of the status and quality of FCC is vital. Accurate description of the relationship between process and quality variables is the basis of quality-driven monitoring. Many process variables affect the quality of FCC; some of these effects are linear, and others are nonlinear. We propose a combination method from the perspective of linearity and nonlinearity to improve the monitoring performance of FCC quality. Partial least squares (PLS) is initially used to extract linear features, and its residual space is saved as the input of the deep feedforward neural network (DFNN). DFNN is then used to extract nonlinear features for the further decomposition of subspaces. The PLS-DFNN method accurately describes processes involving linearity and nonlinearity. We construct three monitoring statistics to characterize the types of faults. The proposed method proves its excellent effect on a numerical simulation data set. It effectively distinguishes the types of faults on the Tennessee Eastman process data set, and the fault detection rate is superior to other related methods. Finally, we apply this method to the actual FCC and verify the superiority of this combination.

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Section: Experiments and Discussionmentioning

confidence: 99%

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network

Yang

Yan

et al. 2023

Transactions of the Institute of Measurement and Control

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“…figure 4 (f) shows that I 2 n didn't alarm. According to Zhou et al [5] and Wang et al [8], there are five quality irrelevant faults (IDV 3,4,9,11,15). False alarm rates (FARs) of I 2 q for 5 quality-unrelated faults are shown in Table 4, while fault detection rates (FDRs) of I 2 n and SPE in these five fault conditions are displayed in Table 5.…”

Section: B the Tennessee Eastmation Process Simulationmentioning

confidence: 99%

“…These assumptions can be easily broken in reality. Therefore, Li et al [10] and Kong et al [11] developed dynamic T-PLS approach to form quality relevant data-driven modeling method for multivariate dynamic process monitoring. When it comes to nonlinear problem, Sheng et al [12] presented a novel method named concurrent kernel PLS (CKPLS), Yin et al [13] and Zhou et al [14] combined locally weighted projection regression (LWPR) with improved PLS to model nonlinear process and design monitoring statistics.…”

Section: Introductionmentioning

confidence: 99%

Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

2019

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Partial least squares (PLS) and linear regression methods have been widely utilized for qualityrelated fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes. INDEX TERMS Quality-related fault detection, independent component regression, orthogonal signal correction, non-Gaussian process, QR decomposition.

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Quality-Related Dynamic Process Monitoring: Part I

Kong,

Luo,

Feng

2024

Engineering Applications of Computational Methods

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Quality-Related and Process-Related Fault Monitoring With Online Monitoring Dynamic Concurrent PLS

Cited by 8 publications

References 33 publications

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network

Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

Quality-Related Dynamic Process Monitoring: Part I

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