Fault diagnosis by Locality Preserving Discriminant Analysis and its kernel variation

“…This means that the generated features from FDA are discriminative in nature. Kernel FDA, its nonlinear extension, is used extensively such as in [74,75,80,92,98,102,103,105,118,130,151,169,175,183,195,204,222,232,238,258,266,294]. One variant of FDA is exponential discriminant analysis (EDA) which solves the singularity problem in the FDA covariance matrices by taking their exponential forms [281,283].…”

Section: Diagnosis By Fault Classificationmentioning

confidence: 99%

“…Typically, k = 5 or 10. Grid search is a common approach for the tuning stage, where the kernel parameters are chosen from a grid of candidates, as did [67,79,98,121,124,141,151,170,171,195,201,215,259]. Based on a recent study by Fu et al [68], cross-validation was found to yield better estimates of the kernel parameters than the empirical approach.…”

Section: Kernel Parameter Selectionmentioning

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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“…Then they are sent to the vapour‐liquid separator. In this unit operation, most main products are separated and unsegregated main product and a small part of byproducts are sent to the reactor by the compressor …”

Section: Te Process Case Studymentioning

confidence: 99%

An effective fault diagnosis approach based on optimal weighted least squares support vector machine

Liu

Wang

et al. 2017

Can J Chem Eng

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Fault diagnosis is always a vital technology in the chemical industry and is influenced by a large number of process variables in the practical manufacturing process. Thus, an effective diagnosis method is crucial in practical chemical processes. A novel fault diagnosis method is proposed based on the weighted least squares support vector machine (WLSVM) to deal with the small sample and non‐linear partition data. It provides the strong potential in predicting faults, especially by further employing a suitable particle swarm optimization (PSO) algorithm to determine the most important parameters of the punishment factor and the Gaussian RBF kernel, which has the advantages of high accuracy and low false alarm rate. PSO‐WLSSVM is further accomplished with the data from the classic benchmark test set Tennessee Eastman (TE) process. Also, the models SVM, PSO‐SVM, and PSO‐LSSVM are employed on the TE process to compare the performance of PSO‐WLSSVM. The results show the validity of the proposed approach on the TE process.

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Fault diagnosis by Locality Preserving Discriminant Analysis and its kernel variation

Cited by 18 publications

References 25 publications

Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection

Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection

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

An effective fault diagnosis approach based on optimal weighted least squares support vector machine

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