Monitoring of a machining process using kernel principal component analysis and kernel density estimation
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Intelligent Manufacturing"
DOI: 10.1007/s10845-019-01504-w
Abstract: Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and… read more here.
Keywords: machining process; principal component; kernel principal; density ... See more keywords
Faults detection and identification in PV array using kernel principal components analysis
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Energy and Environmental Engineering"
DOI: 10.1007/s40095-021-00416-x
Abstract: The exponential growth of the photovoltaic system installations also requires an adequate maintenance and supervision system to ensure the continuity of service of the system. Conventional protection systems for electrical systems have shown their shortcomings… read more here.
Keywords: array; analysis; system; detection ... See more keywords
Fault detection and estimation using kernel principal component analysis
Sign Up to like & getrecommendations! Published in 2017 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2017.08.212
Abstract: Abstract The principal component analysis (PCA) is a linear technique widely used to retrieve a subspace that maximizes the variance of the data, making the presence of a fault easy to detect. Nevertheless, the real… read more here.
Keywords: technique; fault; principal component; kernel principal ... See more keywords
Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets
Sign Up to like & getrecommendations! Published in 2021 at "ACS Omega"
DOI: 10.1021/acsomega.0c06039
Abstract: The Internet environment has provided massive data to the actual industrial production process. It not only has large amounts of data but also has a high data dimension, which brings challenges to the traditional statistical… read more here.
Keywords: kernel; principal component; kernel principal; matrix ... See more keywords
Thyristor State Evaluation Method Based on Kernel Principal Component Analysis
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3159711
Abstract: The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which… read more here.
Keywords: analysis; principal component; thyristor; evaluation ... See more keywords
Transient Electromagnetic Inversion: An ICDE-Trained Kernel Principal Component OSELM Approach
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3112192
Abstract: The traditional extreme learning machine (ELM) inversion of transient electromagnetic method (TEM) based on random initial weights is known to be inept for its low-computational efficiency and poor generalization performance. To solve these problems, a… read more here.
Keywords: inversion; kernel principal; principal component; icde ... See more keywords
Exactly Robust Kernel Principal Component Analysis
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2909686
Abstract: Robust principal component analysis (RPCA) can recover low-rank matrices when they are corrupted by sparse noises. In practice, many matrices are, however, of high rank and, hence, cannot be recovered by RPCA. We propose a… read more here.
Keywords: component analysis; robust kernel; kernel principal; principal component ... See more keywords
Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Sign Up to like & getrecommendations! Published in 2017 at "Advances in Electrical and Computer Engineering"
DOI: 10.4316/aece.2017.04005
Abstract: In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems. R ... read more here.
Keywords: principal component; fault localization; kernel principal; component analysis ... See more keywords
Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components
Sign Up to like & getrecommendations! Published in 2018 at "Statistica Sinica"
DOI: 10.5705/ss.202016.0369
Abstract: We study the estimation of conditional mean regression functions through the so-called subset-based kernel principal component analysis (KPCA). Instead of using one global kernel feature space, we project a target function into different localized kernel… read more here.
Keywords: subset based; estimation; regression; based kernel ... See more keywords