multivariate process monitoring Latest Research Papers

Improved Multiscale Multivariate Process Monitoring Methods

2021 American Control Conference (ACC) ◽

10.23919/acc50511.2021.9483359 ◽

2021 ◽

Author(s):

M. Ziyan Sheriff ◽

M. Nazmul Karim ◽

Costas Kravaris ◽

Hazem N. Nounou ◽

Mohamed N. Nounou

Keyword(s):

Process Monitoring ◽

Monitoring Methods ◽

Multivariate Process ◽

Multivariate Process Monitoring

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Reducing response delay in multivariate process monitoring by a stacked long-short term memory network and real-time contrasts

Computers & Industrial Engineering ◽

10.1016/j.cie.2020.107052 ◽

2021 ◽

Vol 153 ◽

pp. 107052

Author(s):

Chao-Lung Yang ◽

Hendri Sutrisno

Keyword(s):

Real Time ◽

Process Monitoring ◽

Short Term Memory ◽

Response Delay ◽

Short Term ◽

Multivariate Process ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory ◽

Multivariate Process Monitoring

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Fault detection of wind turbines via multivariate process monitoring based on vine copulas

Renewable Energy ◽

10.1016/j.renene.2020.06.091 ◽

2020 ◽

Vol 161 ◽

pp. 939-955

Author(s):

Qifa Xu ◽

Zhenhua Fan ◽

Weiyin Jia ◽

Cuixia Jiang

Keyword(s):

Fault Detection ◽

Process Monitoring ◽

Wind Turbines ◽

Multivariate Process ◽

Vine Copulas ◽

Multivariate Process Monitoring

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A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring

IEEE Transactions on Control Systems Technology ◽

10.1109/tcst.2018.2816903 ◽

2019 ◽

Vol 27 (4) ◽

pp. 1780-1787 ◽

Cited By ~ 11

Author(s):

Yuan Xu ◽

Sheng-Qi Shen ◽

Yan-Lin He ◽

Qun-Xiong Zhu

Keyword(s):

Process Monitoring ◽

Hybrid Method ◽

Multivariate Process ◽

Multivariate Process Monitoring

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Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.8b04081 ◽

2019 ◽

Vol 58 (9) ◽

pp. 3782-3796 ◽

Cited By ~ 8

Author(s):

Yang Zhou ◽

Shaojun Li ◽

Ning Xiong

Keyword(s):

Ensemble Learning ◽

Process Monitoring ◽

Multivariate Process ◽

Vine Copula ◽

Multivariate Process Monitoring

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Multivariate Process Monitoring and Fault Identification Using Convolutional Neural Networks

Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018 ◽

10.1007/978-981-13-3402-3_22 ◽

2019 ◽

pp. 197-208

Author(s):

Xiaoyun Zheng ◽

Jianbo Yu

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Process Monitoring ◽

Fault Identification ◽

Multivariate Process ◽

Multivariate Process Monitoring

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The Data-Driven Multivariate Process Monitoring and Diagnosis of Rides in an Amusement Park

2018 12th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID) ◽

10.1109/icasid.2018.8693125 ◽

2018 ◽

Author(s):

Hongguang Wen ◽

Yin Zhao ◽

Wenjun Chen

Keyword(s):

Process Monitoring ◽

Data Driven ◽

Multivariate Process ◽

Amusement Park ◽

Monitoring And Diagnosis ◽

Multivariate Process Monitoring

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Enhancing Quality of Multivariate Process Monitoring Based on Vine Copula and Active Learning Strategy

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.7b05128 ◽

2018 ◽

Vol 57 (23) ◽

pp. 7961-7974 ◽

Cited By ~ 5

Author(s):

Yang Zhou ◽

Xiang Ren ◽

Shaojun Li

Keyword(s):

Active Learning ◽

Process Monitoring ◽

Learning Strategy ◽

Multivariate Process ◽

Vine Copula ◽

Active Learning Strategy ◽

Multivariate Process Monitoring

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Optimal false alarm controlled support vector data description for multivariate process monitoring

Journal of Process Control ◽

10.1016/j.jprocont.2017.10.012 ◽

2018 ◽

Vol 65 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Younghoon Kim ◽

Seoung Bum Kim

Keyword(s):

False Alarm ◽

Process Monitoring ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Multivariate Process ◽

Data Description ◽

Multivariate Process Monitoring

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Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23037 ◽

2017 ◽

Vol 96 (5) ◽

pp. 1127-1141 ◽

Cited By ~ 3

Author(s):

Lei Wang ◽

Xiaogang Deng

Keyword(s):

Principal Component Analysis ◽

Process Monitoring ◽

Principal Component ◽

Component Analysis ◽

Multivariate Process ◽

Variable Weight ◽

Multivariate Process Monitoring

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multivariate process monitoring
Recently Published Documents

TOTAL DOCUMENTS

H-INDEX

Improved Multiscale Multivariate Process Monitoring Methods

Reducing response delay in multivariate process monitoring by a stacked long-short term memory network and real-time contrasts

Fault detection of wind turbines via multivariate process monitoring based on vine copulas

A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring

Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Multivariate Process Monitoring and Fault Identification Using Convolutional Neural Networks

The Data-Driven Multivariate Process Monitoring and Diagnosis of Rides in an Amusement Park

Enhancing Quality of Multivariate Process Monitoring Based on Vine Copula and Active Learning Strategy

Optimal false alarm controlled support vector data description for multivariate process monitoring

Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring

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multivariate process monitoringRecently Published Documents

TOTAL DOCUMENTS

H-INDEX

Improved Multiscale Multivariate Process Monitoring Methods

Reducing response delay in multivariate process monitoring by a stacked long-short term memory network and real-time contrasts

Fault detection of wind turbines via multivariate process monitoring based on vine copulas

A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring

Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Multivariate Process Monitoring and Fault Identification Using Convolutional Neural Networks

The Data-Driven Multivariate Process Monitoring and Diagnosis of Rides in an Amusement Park

Enhancing Quality of Multivariate Process Monitoring Based on Vine Copula and Active Learning Strategy

Optimal false alarm controlled support vector data description for multivariate process monitoring

Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring

multivariate process monitoring
Recently Published Documents