Process Fault Detection Using Directional Kernel Partial Least Squares

Zhang, Yingwei; Du, Wenyou; Fan, Yunpeng; Zhang, Lingjun

doi:10.1021/ie501502t

Cited by 35 publications

(18 citation statements)

References 52 publications

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“…The application was in the HSMP, wherein both quality-related and non-quality-related faults were investigated. Further developments on kernel PLS can be found in [146,160,163,164,168,173,196,197,199,206,229,231,242,243,268,284]. Concurrent PLS was also proposed to solve some drawbacks of the T-PLS.…”

Section: Quality-relevant Monitoringmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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Section: Quality-relevant Monitoringmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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“…[17,18] Kernel learning methods map the process data into a high-dimension feature space and subsequently perform linear transformation in the feature space. [17,19,20] The underlying assumption is that data that cannot be linearly separated become separable in a high-dimension space. However, this assumption does not always hold, and the representative ability may be limited.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven nonlinear chemical process fault diagnosis based on hierarchical representation learning

Yang

Jiang

2020

Can J Chem Eng

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Representation extraction is crucial in data‐driven process monitoring, and deep neural network (DNN) is an efficient tool for extracting representations from considerable process data. This study proposes a hierarchical representation learning (HRL) method that integrates the deep belief neural (DBN) network and support vector data description (SVDD) for efficient nonlinear chemical process fault diagnosis. First, hierarchical representations containing meaningful process information are generated through a DBN network by utilizing generally massive normal operating process data. Second, an SVDD‐based decision‐making system is constructed using generally small‐sized faulty data. Three experimental studies are then conducted. A comparison of results with those of several state‐of‐the‐art methods reveal the suitability of the HRL method for process monitoring due to its two main advantages. First, DNN has a superior representative ability and generates representations with richer process information than conventional data‐driven methods. Second, the HRL method utilizes available process data and is suitable for practical conditions in which considerable normal operating data but limited small‐sized faulty data are available.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Heng Wang . in modern industrial processes, data-driven FD techniques have been discussed widely by researchers [2]- [9]. Partial least squares (PLS) regression is the most used data-driven technique in modeling and FD of industrial processes, and it has proved excellent performance [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS

et al. 2020

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Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS. INDEX TERMS Kernel PLS (KPLS), interval KPLS (IKPLS), interval reduced KPLS (IRKPLS), fault detection (FD), uncertain systems.

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Process Fault Detection Using Directional Kernel Partial Least Squares

Cited by 35 publications

References 52 publications

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Data‐driven nonlinear chemical process fault diagnosis based on hierarchical representation learning

Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS

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