Multiscale classification and its application to process monitoring

Liu, Yuming; Ye, Lu-bin; Zheng, Ping-you; Shi, Xiangrong; Hu, Bin; Liang, Jun

doi:10.1631/jzus.c0910430

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…Although it shows limited performance in nonlinear systems due to its linearity, it is better suited to classification problems [88]. Liu et al [89] proposed a multiscale classification method to obtain the most discriminatory characteristics of the scale. The effects of feature extraction investigated the classifier performance, and a multiscale classifier was developed to classify the faults better.…”

Section: B Multiscale Methods For Nonlinear Process Monitoringmentioning

confidence: 99%

“…SVM is a well-known classification tool, proposed initially by Cortes and Vapnik [109]. Liu et al [89] proposed a multiscale fault diagnosis method and applied the SVM classifier based on classification distance, using 4-fold to obtain the optimal parameters. Nor et al [91] incorporated the SVM classifier with multiscale KFD, and the performance accuracy was compared to the multiscale KFD-GMM of the faults in the TEP.…”

Section: E Multiscale Methods For Fault Diagnosismentioning

confidence: 99%

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Review of Multiscale Methods for Process Monitoring, With an Emphasis on Applications in Chemical Process Systems

et al. 2022

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Process monitoring has played an increasingly significant role in ensuring safe and efficient manufacturing operations in process industries over the past several years. Chemical process data is highly correlated and has multiscale characteristics in general. To overcome this concern, extensive work has been made for multiscale process monitoring for process plants during the past two decades. The recent success of multiscale methods in monitoring and controlling manufacturing processes has sparked interest in investigating these methods for process monitoring. This article aims to present a concise and critical overview of the applications of multiscale process monitoring methods in chemical processes. First objective is to identify the importance of multiscale methods for process monitoring. The second and main objective is the statistical and critical analysis for methods implementation, application area, types of data used, and various issues mentioned by previous researchers. In addition, the most important critical issues have been identified, and the capabilities and limitations of each method are discussed and highlighted. The reported literature focused mainly on fault detection and did not investigate the root-cause diagnosis of the detected faults. Further, the challenges and prospects in multiscale process monitoring in the chemical process industry have been featured for advancement.

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Section: B Multiscale Methods For Nonlinear Process Monitoringmentioning

confidence: 99%

Section: E Multiscale Methods For Fault Diagnosismentioning

confidence: 99%

Review of Multiscale Methods for Process Monitoring, With an Emphasis on Applications in Chemical Process Systems

et al. 2022

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“…The introduction of a momentum parameter is made to avoid the algorithm converging to a local minimum by modifying the step change of the weight (Shukla et al, 2010). Support vector machine (SVM) uses a classification distance as a criterion to determine the occurrence of a failure mode (Liu et al, 2010;Muralidharan et al, 2014;Yin et al, 2014;Hang et al, 2016;Swetapadma and Yadav, 2016). In its classical version, this method determines a hyper-plane that optimally divides data corresponding to two different classes through training datasets (Kishore et al, 2016).…”

Section: Data-driven Techniquesmentioning

confidence: 99%

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Ruiz-Arenas

Rusák

Horváth

et al. 2019

Frontiers Inf Technol Electronic Eng

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Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

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“…TE process is a well-known benchmark for testing the performance of various fault detection methods (Lyman and Georgakist, 1995;Yu and Qin, 2008;Liu et al, 2010;Chen and Yan, 2012;Stubbs et al, 2012). A flowchart of the TE process is shown schematically in Fig.…”

Section: Tennessee Eastman (Te) Processmentioning

confidence: 99%

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Tong

Yan

2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T 2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

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Multiscale classification and its application to process monitoring

Cited by 14 publications

References 23 publications

Review of Multiscale Methods for Process Monitoring, With an Emphasis on Applications in Chemical Process Systems

Review of Multiscale Methods for Process Monitoring, With an Emphasis on Applications in Chemical Process Systems

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

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