Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

Jiang, Qingchao; Yan, Xuefeng; Lv, Zhaomin; Guo, Meijin

doi:10.1007/s11814-013-0034-7

Cited by 18 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Entropy is a concept representing the amount of information contained. Studies have shown that by introducing entropy information, the KECA algorithm can achieve better nonlinear processing results than the KPCA algorithm [23][24][25]. Therefore, drawing on the principle of KECA, the KPLS algorithm can also be used to extract the eigenvalues and eigenvectors according to the information entropy.…”

Section: Prediction Model Based On Kepls 51 Principles Of Kepls Algor...mentioning

confidence: 99%

A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance

Ren

Gao

et al. 2022

Teh. vjesn.

View full text Add to dashboard Cite

To overcome the problems of wind turbine (WT) degradation assessment, a new kernel entropy method based on supervisory control and data acquisition (SCADA) was proposed. This approach can be used to effectively monitor and assess WT performance degradation. First, a new condition monitoring method based on a kernel entropy component analysis (KECA) was developed for nonlinear data. Then, the squared prediction error (SPE) was used to monitor the WT health state. Due to the diversity and nonlinearity of SCADA data, fault features are easily overwhelmed by other vibration signals. To address this, a new kernel entropy partial least squares (KEPLS) algorithm was introduced. The proposed kernel entropy method improves the performance prediction by considering higher order information. Furthermore, changes in the prediction residual can be used to define certain limits to realize early warning of WT faults. Finally, the method was applied to actual SCADA data of a wind farm. The results show that the method can accurately evaluate the health state of WTs, thus verifying the effectiveness and feasibility of the proposed method.

show abstract

Section: Prediction Model Based On Kepls 51 Principles Of Kepls Algor...mentioning

confidence: 99%

A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance

Ren

Gao

et al. 2022

Teh. vjesn.

View full text Add to dashboard Cite

show abstract

“…KECA is proposed by R. Jenssen, the original idea of the KECA method is introduced in detail [12], [34]. The definition of Renyi quadratic entropy is as follows:…”

Section: Keca Feature Reduction Algorithmmentioning

confidence: 99%

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Zhang

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Control charts are significant diagnostic tools to detect and identify the quality fluctuation of the complex industrial process. In the practical production process, attention is being paid to the monitoring of mixture control charts, which usually coupled by two or more basic control charts modes. This research is to present a hybrid pattern recognition method for mixture control charts. The proposed method mainly covers the feature fusion extraction (FFE) and kernel extreme learning machine (KELM) with modified grey wolf optimizer (MGWO). The FFE module applies the original data and their shape and statistical features as the features, then uses kernel entropy component analysis to reduce the feature dimension and extract valid features. One significant difficulty of KELM is to get suitable parameters like the penalty parameter and the kernel function parameter value. MGWO is established to the optimal tuning of KELM parameters, which improves the population initialization and nonlinear convergence factor of traditional grey wolf optimizer. The proposed methodology is promising to obtain a better classification recognition rate, less computational time and achieves more stable results in the pattern recognition problem of mixture control charts. INDEX TERMS Control chart patterns recognition, feature fusion extraction, kernel extreme learning machine, kernel entropy component analysis, modified grey wolf optimizer.

show abstract

“…It has proven useful in different applications, e.g. remote sensing data analysis [3], [4], [5], face recognition [6], chemical processes modelling [7], high-dimensional celestial spectra reduction [8] and audio processing [9]. Several extensions have been proposed for feature selection [10], class-dependent feature extraction [11] and semisupervised learning as well [12].…”

Section: Introductionmentioning

confidence: 99%

Optimized Kernel Entropy Components

Izquierdo‐Verdiguier

Laparra

Jenssen

et al. 2017

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

show abstract

Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

Cited by 18 publications

References 25 publications

A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance

A Kernel Entropy Method and its Application in Monitoring and Assessment of Wind Turbine Degradation Performance

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Optimized Kernel Entropy Components

Contact Info

Product

Resources

About