Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring

Jaffel, Ines; Taouali, Okba; Harkat, Mohamed-Faouzi; Messaoud, Hassani

doi:10.1007/s00170-016-8987-4

Cited by 34 publications

(12 citation statements)

References 29 publications

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“…To select more sensitive features and decrease the computational burden, PCA is used to reduce the dimension of features. In general, the threshold of PCA method is set to 0.85 [37]. Through calculation, we find that the cumulative contribution rate has exceeded 95% when the first seven principal components is chosen.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Wang

2020

Sensors

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Performance degradation prediction plays a key role in realizing aviation pump health management and condition-based maintenance. Thus, this paper proposes a new approach that combines a Gaussian mixture model (GMM) and optimized support vector regression (SVR) to predict aviation pumps’ degradation processes based on the pump outlet pressure signals. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not fully utilized, some useful IMF components are firstly chosen, and the corresponding multi-domain features are extracted from each selected component. Considering that it is not the case that all features are equally sensitive to degradation assessment, PCA is used to select more sensitive degradation features. Since the distribution of these extracted features is a stochastic process in feature space, meanwhile, self-information quantity can describe the uncertainty of system by measuring the average information quantity contained in the probability distribution, self-information quantity based on GMM is defined as degradation index (DI) to describe the degradation degree of the pump quantitatively. Finally, an SVR model is constructed to predict the degradation status of the pump. To achieve higher prediction accuracy, phase space reconstruction theory is first employed to determine the number of the inputs of the SVR model, then a new method combining particle swarm optimization (PSO) with grid search (GS) is developed to optimize the parameters of the SVR model. Finally, both the online data and historical data are utilized for the construction of the SVR model, respectively. The effectiveness of the proposed approach is validated by full life cycle data collected from an aviation pump test rig. The results demonstrate that the DI extracted from pump outlet pressure signals can effectively identify and track the current deterioration stage, and the established SVR model has better prediction ability when compared with previously published methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Wang

2020

Sensors

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“…6) Characterization techniques such as the proposed method provide an effective tool for obtaining system information through kernel extraction. Comparing [40].…”

Section: Discussionmentioning

confidence: 99%

Extraction of the Third-Order 3x3 MIMO Volterra Kernel Outputs Using Multitone Signals

Khan

Zenteno

Händel

et al. 2018

IEEE Trans. Microwave Theory Techn.

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This paper uses multitone signals to simplify the analysis of 3×3 multiple-input multiple-output (MIMO) Volterra systems by isolating the third-order kernel outputs from each other. Multitone signals fed to an MIMO Volterra system yield a spectrum that is a permutation of the sums of the input signal tones. This a priori knowledge is used to design multitone signals such that the third-order kernel outputs are isolated in the frequency domain. The signals are designed by deriving the conditions for the offset and spacing of the input frequency grids. The proposed technique is then validated for the six possible configurations of a 3 × 3 RF MIMO transmitter impaired by crosstalk effects. The proposed multitone signal design is used to extract the third-order kernel outputs, and their relative contributions are analyzed to determine the dominant crosstalk effects for each configuration.

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“…Meanwhile, Guo et al [278] reformulated kernel PCA itself to sparsify the projection matrix using elastic net regression. Other techniques for sample subset selection includes feature sample extraction [73], the use of fuzzy C-means clustering [159], reduced KPCA [207], partial KPCA [249], and dictionary learning [246,250,270,271,274]. These methods are efficient enough to warrant an online adaptive implementation (see Section 4.10).…”

Section: Fast Computation Of Kernel Featuresmentioning

confidence: 99%

“…Meanwhile, Jaffel et al [191] proposed a moving window reduced kernel PCA, where "reduced" pertains to an approach for easing the computational burden as discussed in Section 4.8. Other related works that utilize the moving window concept can be found in [190,[207][208][209]238,293]. A different adaptive approach is to use multivariate EWMA to update any part of the model, such as the kernel matrix, its eigen-decomposition, or the statistical indices [116,132,179,224,253,281,283,292].…”

Section: Time-varying Behavior and Adaptive Kernel Computationmentioning

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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Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring

Cited by 34 publications

References 29 publications

Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Extraction of the Third-Order 3x3 MIMO Volterra Kernel Outputs Using Multitone Signals

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

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