Filters








710 Hits in 5.8 sec

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis

Xiaogang Deng, Xuemin Tian, Sheng Chen, Chris J. Harris
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j6amxna35bbs5p42wy5crllu2i" style="color: black;">IEEE Transactions on Neural Networks and Learning Systems</a> </i> &nbsp;
a serial model structure, which we refer to as serial principal component analysis (SPCA).  ...  based on KPCA alone, in terms of nonlinear process fault detection and identification.  ...  , which we refer to as serial principal component analysis (SPCA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2016.2635111">doi:10.1109/tnnls.2016.2635111</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28026785">pmid:28026785</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qlbt24hoh5cmxj62g54vfachzm">fatcat:qlbt24hoh5cmxj62g54vfachzm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724062106/https://eprints.soton.ac.uk/418443/1/SPCAlatexV3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9f/49/9f49aa3d4ff5790174cdcf4ed473aa4c7b24fd25.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2016.2635111"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Monitoring of Distillation Column Based on Indiscernibility Dynamic Kernel PCA

Qiang Gao, Yong Chang, Zhen Xiao, Xiao Yu
<span title="">2016</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
In this paper, a new indiscernibility dynamic kernel principal component analysis (I-DKPCA) method is presented and applied to distillation column.  ...  Aiming at complicated faults detection of distillation column industrial process, it has faced a grave challenge.  ...  Principal component analysis (PCA) is one of most widely used models in statistical process monitoring [15] [16] [17] [18] [19] [20] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2016/9567967">doi:10.1155/2016/9567967</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xck2n4gyszae7n4kvoj4ndu34q">fatcat:xck2n4gyszae7n4kvoj4ndu34q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503115828/http://downloads.hindawi.com/journals/mpe/2016/9567967.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/01/9d01529b94109cce49f5cc8c800ae0dd33061e11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2016/9567967"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Fault detection and identification based on explicit polynomial mapping and combined statistic in nonlinear dynamic processes

Liangliang Shang, Kexin Shi, Chen Ma, Aibing Qiu, Liang Hua
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Compared with the results of PCA, CVA, kernel principal component analysis (KPCA), nonlinear dynamic principal component analysis (NDPCA) and kernel entropy component analysis (KECA), the proposed method  ...  on explicit polynomial mapping and combined statistic for detecting and identifying faults in nonlinear dynamic processes.  ...  [23] proposed a nonlinear dynamic process monitoring methodology based on dynamic kernel principal component analysis (DKPCA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3124948">doi:10.1109/access.2021.3124948</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eliaxkw3sjfmleg6cmho5yrice">fatcat:eliaxkw3sjfmleg6cmho5yrice</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211108022421/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09598819.pdf?tp=&amp;arnumber=9598819&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a7/79/a77959bf10d64a9b6ed3b731e907af6d72ae9138.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3124948"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Research on Micro-Fault Detection and Multiple-Fault Isolation for Gas Sensor Arrays Based on Serial Principal Component Analysis

Yonghui Xu, Ruotong Meng, Zixuan Yang
<span title="2022-05-31">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
component analysis (PCA) in the linear part and the advantages of kernel principal component analysis (KPCA) in the nonlinear part.  ...  In order to solve the problem of low detection accuracy of micro-faults in gas sensor arrays, this paper adopts the serial principal component analysis (SPCA) method, which combines the advantages of principal  ...  Fault Detection Stage 2.2.1. Serial Principal Component Analysis SPCA is a hybrid linear and nonlinear statistical modeling method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics11111755">doi:10.3390/electronics11111755</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/th66zkeqx5dlpnqytcpqjgrqae">fatcat:th66zkeqx5dlpnqytcpqjgrqae</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220610071617/https://mdpi-res.com/d_attachment/electronics/electronics-11-01755/article_deploy/electronics-11-01755-v2.pdf?version=1654595193" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/08/6f/086f8b1dc63c8df8a07ccd01685e413699de832f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics11111755"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Canonical Variate Nonlinear Principal Component Analysis for Monitoring Nonlinear Dynamic Processes

Liangliang Shang, Aibing Qiu, Peng Xu, Feng Yu
<span title="2022-01-20">2022</span> <i title="Society of Chemical Engineers, Japan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eavoisg3jvbbtbl7oaabszlwom" style="color: black;">Journal of Chemical Engineering of Japan</a> </i> &nbsp;
Kernel principal component analysis (KPCA) based on the radial basis function, has already been applied in numerous nonlinear industrial processes.  ...  The rst k principal components and the remaining residual vectors are obtained in the feature space via conventional principal component analysis for fault detection.  ...  Alcala and Qin (2010) proposed reconstruction-based contributions (RBCs) to diagnose faults in nonlinear principal component spaces based on KPCA.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1252/jcej.19we080">doi:10.1252/jcej.19we080</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vdleslpyhvgn3ekpdaypqkoyyy">fatcat:vdleslpyhvgn3ekpdaypqkoyyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220209071919/https://www.jstage.jst.go.jp/article/jcej/55/1/55_19we080/_pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/ec/a9ec1b3cb26482aeeb7aafdfa1620018e54148f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1252/jcej.19we080"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Fault Detection Approach Based on Weighted Principal Component Analysis Applied to Continuous Stirred Tank Reactor

Shanmao Gu, Yunlong Liu, Ni Zhang, De Du
<span title="2015-10-07">2015</span> <i title="Bentham Science Publishers Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g7svpphnp5gwjhgwkecqf6ewza" style="color: black;">The Open Mechanical Engineering Journal</a> </i> &nbsp;
Fault detection approach based on principal component analysis (PCA) may perform not well when the process is time-varying, because it can cause unfavorable influence on feature extraction.  ...  The monitoring statistical indices are based on WPCA model and their confidence limits are computed by kernel density estimation (KDE).  ...  Ku [10] proposed dynamic PCA (DPCA) in which serial correlation of data is considered. A multi-fault diagnosis method for sensor systems based on PCA was presented in [11] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/1874155x01509010966">doi:10.2174/1874155x01509010966</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e3snuutws5elplrdf3mofkfhze">fatcat:e3snuutws5elplrdf3mofkfhze</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428135138/https://benthamopen.com/contents/pdf/TOMEJ/TOMEJ-9-966.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/83/6483b63421cbf5024529fde17c3117caba2f0a61.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/1874155x01509010966"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Sensor Monitoring Of The Concentrations Of Different Gases Present In Synthesis Of Ammonia Based On Multi-Scale Entropy And Multivariate Statistics

S. Aouabdi, M. Taibi
<span title="2015-05-01">2015</span> <i title="Zenodo"> Zenodo </i> &nbsp;
present in the synthesis of Ammonia and soft-sensor based on Principal Component Analysis (PCA).  ...  This paper presents powerful techniques for the development of a new monitoring method based on multi-scale entropy (MSE) in order to characterize the behaviour of the concentrations of different gases  ...  The need to analyze high-dimensional and correlated process data has led to the development of many monitoring schemes based on principal component analysis (PCA) as modeling technique that transforms  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1100930">doi:10.5281/zenodo.1100930</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dfd6j7zif5ha7ijmm2jzdxtxp4">fatcat:dfd6j7zif5ha7ijmm2jzdxtxp4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201228181642/https://zenodo.org/record/1100931/files/10001353.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/25/ee2530b43930f6081f1282c009804896af778b11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1100930"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

CONSTRUCTION OF AN ELECTROMECHANICAL AUTOMATION AND FAULT DIAGNOSIS SYSTEM USING DATA-DRIVEN TECHNOLOGY IN THE CONTEXT OF BIG DATA

<span title="2021-11-29">2021</span> <i title="INCDMTM"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rtir75wcsjajtbzoztuya7kexu" style="color: black;">International Journal of Mechatronics and Applied Mechanics</a> </i> &nbsp;
Secondly, the principal component analysis (PCA) method is introduced, and accordingly, the optimized dynamic principal component analysis (DPCA) method and several common sensor faults are proposed.  ...  Thirdly, the specific steps of the PCA and DPCA methods are discussed in sensor faults diagnosis, respectively. The PCA method can study the multivariate nonlinear data concurrently.  ...  The main methods include principal component analysis (PCA), factor analysis (FA), and canonical variate analysis (CVA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17683/ijomam/issue10/v2.23">doi:10.17683/ijomam/issue10/v2.23</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wipqjvy4azfl7oewmk35kf36zm">fatcat:wipqjvy4azfl7oewmk35kf36zm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220131235432/https://ijomam.com/wp-content/uploads/2021/11/pag.-199-208_CONSTRUCTION-OF-AN-ELECTROMECHANICAL-AUTOMATION.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a2/63/a263234ac821a852db8e5018511d63966cd0fdbd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17683/ijomam/issue10/v2.23"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Bearing Fault Feature Selection Method Based on Weighted Multidimensional Feature Fusion

Yazhou Li, Wei Dai, Weifang Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
, correlation analysis and principal component analysis-weighted load evaluation (PCA-WLE) is put forward in this paper for selecting sensitive features.  ...  Rolling bearing is one of the most critical components in rotating machinery, so in order to efficiently select features, reduce feature dimensions and improve the correctness of fault diagnosis, a feature  ...  PRINCIPAL COMPONENT ANALYSIS AND WEIGHTED LOAD EVALUATION 1) PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) is a commonly used data processing and analysis method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2967537">doi:10.1109/access.2020.2967537</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fzd53rl6fjeupb7qmiubfw2hrq">fatcat:fzd53rl6fjeupb7qmiubfw2hrq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108124406/https://ieeexplore.ieee.org/ielx7/6287639/8948470/08962035.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/35/0e/350e6ec26ed3877f0813d9e63965de83e0c9df05.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2967537"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

Cheng Ji, Wei Sun
<span title="2022-02-10">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vt2hc3xcijfofb7cwnxv4hhszi" style="color: black;">Processes</a> </i> &nbsp;
Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation.  ...  In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data.  ...  Sparse principal component analysis (SPCA) was proposed to solve this problem by restricting some of the loadings on principal components to zero, and therefore main parts of principal components can be  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr10020335">doi:10.3390/pr10020335</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bicixwwjjbfczdkba62a7u2jhe">fatcat:bicixwwjjbfczdkba62a7u2jhe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220501171153/https://mdpi-res.com/d_attachment/processes/processes-10-00335/article_deploy/processes-10-00335.pdf?version=1644488659" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/49/86492fdd194cdfd9181ad2d093a0513901c5d674.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr10020335"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

A Combination of Dynamic Simulation and Dynamic Time Warping for Fault Diagnosis of Chemical Process Startups

Suli Sun, Xiaoyun Song, Zhiguo Liu, Wende Tian, Chuankun Li
<span title="">2020</span> <i title="AIDIC Servizi S.r.l."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5re6yojrfja7da72zowrdasgca" style="color: black;">Chemical Engineering Transactions</a> </i> &nbsp;
The residual vector under normal conditions is used to develop principal component analysis (PCA) model.  ...  This paper proposed a hybrid fault diagnosis strategy based on dynamic simulation and dynamic time warping (DTW) to probe transient degradation of startup performance.  ...  PenSim simulator gives a nonlinear and unsteady state process and is suitable for fault diagnosis study on startup process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3303/cet2081051">doi:10.3303/cet2081051</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/7fa899500ff44706aca3737974707ab3">doaj:7fa899500ff44706aca3737974707ab3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6zmuprwrgvg5hpakt26ezeayim">fatcat:6zmuprwrgvg5hpakt26ezeayim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201205123544/https://www.aidic.it/cet/20/81/051.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b0/0a/b00a6c41a4b16715e938f03c2bb67beb9acd43de.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3303/cet2081051"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

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

Karl Ezra Pilario, Mahmood Shafiee, Yi Cao, Liyun Lao, Shuang-Hua Yang
<span title="2019-12-23">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vt2hc3xcijfofb7cwnxv4hhszi" style="color: black;">Processes</a> </i> &nbsp;
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.  ...  Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set.  ...  Nonlinear principal component analysis based on principal curves and neural networks. Comput. Chem. Eng. 1996, 20, 65-78. [CrossRef] 49. Hornik, K.; Stinchcombe, M.; White, H.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr8010024">doi:10.3390/pr8010024</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7qnidseekjgj5epeceaezzggrm">fatcat:7qnidseekjgj5epeceaezzggrm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191225014224/https://res.mdpi.com/d_attachment/processes/processes-08-00024/article_deploy/processes-08-00024.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8e/0a/8e0a269011cdfd5c92adca90ce23dc5f49f17629.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr8010024"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Process Monitoring via Key Principal Components and Local Information based Weights

Bing Song, Xinggui Zhou, Shuai Tan, Hongbo Shi, Bo Zhao, Mengling Wang
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Statistical analysis, process monitoring, fault detection, principal component analysis, data analysis.  ...  On the other hand, although the fault information contained in every principal component is different and the principal components are treated equally in traditional PCA-based methods.  ...  problem [13] , dynamic principal component analysis (DPCA) for dealing with serial correlation problem [14] , distributed parallel PCA to monitor large-scale plant-wide processes [15] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2892496">doi:10.1109/access.2019.2892496</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jaiozdbxy5b43jvy5l33kgwvlq">fatcat:jaiozdbxy5b43jvy5l33kgwvlq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428194536/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08631000.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/b3/33b38899b323f44c42de6b9dbcf24229caae0592.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2892496"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Fault Detection and Diagnosis for Plasticizing Process of Single-Base Gun Propellant Using Mutual Information Weighted MPCA under Limited Batch Samples Modelling

Mingyi Yang, Junyi Wang, Yinlong Zhang, Xinlin Bai, Zhigang Xu, Xiaofang Xia, Linlin Fan
<span title="2021-08-12">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kduey52ubfebzlnyz5tuez7sl4" style="color: black;">Machines</a> </i> &nbsp;
based on normalized mutual information weighted multiway principal component analysis (NMI-WMPCA) under limited batch samples modelling was proposed.  ...  NMI-WMPCA utilizes the generalization ability of a multi-model to establish an accurate fault detection model in limited batch samples, and adopts fault diagnosis methods based on a multi-model SPE statistic  ...  Process Monitoring Method Based on Multiway Principal Componet Analysis (MPCA) Principal component analysis (PCA) is a linear dimensionality reduction method based on multivariate projection [43] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/machines9080166">doi:10.3390/machines9080166</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bynxgmjumjd77hhgfqajwtiqjy">fatcat:bynxgmjumjd77hhgfqajwtiqjy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210815221159/https://res.mdpi.com/d_attachment/machines/machines-09-00166/article_deploy/machines-09-00166.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/ed/feed7b4d5f46c87d94874bbdcaa7a3a44b07bda7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/machines9080166"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Fault Detection and Diagnosis in Process Data Using Support Vector Machines

Fang Wu, Shen Yin, Hamid Reza Karimi
<span title="">2014</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xdzjuti5nzejrghubfpezefpqi" style="color: black;">Journal of Applied Mathematics</a> </i> &nbsp;
In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what  ...  is based on SVM-RFE (Recursive Feature Elimination) method.  ...  There are many MSPC tools, for example, principal components analysis (PCA) [5] , dynamic principal components analysis (DPCA), correspondence analysis (CA) [6] , canonical variate analysis (CVA) [7  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2014/732104">doi:10.1155/2014/732104</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sgmex2pu7zd3lpby256jqvzmwq">fatcat:sgmex2pu7zd3lpby256jqvzmwq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190308162731/http://pdfs.semanticscholar.org/f627/bdb66895c5c40360b12fd24f404d06c0ddeb.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/27/f627bdb66895c5c40360b12fd24f404d06c0ddeb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2014/732104"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>
&laquo; Previous Showing results 1 &mdash; 15 out of 710 results