A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix

Jiang, Yi; Huang, Di; He, Haibo; Zhou, Wei; Han, Qi; Li, Taifu

doi:10.1109/tie.2017.2668986

Cited by 56 publications

(17 citation statements)

References 23 publications

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“…The most popular are evolutionary algorithms and swarm intelligence algorithms, which have demonstrated their potential for solving important engineering decision-making problems [31]. The basic formulations of four nature-inspired metaheuristic algorithms: GA, PSO, FFA, and GWO [32]- [34], are described in this section. The derivative-free optimization algorithms used in this work are also briefly presented [11].…”

Section: B Nature-inspired Metaheuristics Algorithmsmentioning

confidence: 99%

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Mello-Román

Hernández

2020

IEEE Access

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Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This paper proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the natureinspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristic algorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression. INDEX TERMS Partial least squares regression, kernel-based method, cross-validation method, and nature-inspired metaheuristic algorithms.

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Section: B Nature-inspired Metaheuristics Algorithmsmentioning

confidence: 99%

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Mello-Román

Hernández

2020

IEEE Access

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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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“…[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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A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix

Cited by 56 publications

References 23 publications

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

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

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

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