Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes

2020 ◽  
pp. 61-67
Author(s):  
Karl Ezra S. Pilario ◽  
Mahmood Shafiee
Keyword(s):  
Kernel Functions ◽  
Nonlinear Processes ◽  
Multivariate Statistical ◽  
Statistical Monitoring

Multivariate statistical monitoring of buildings. Case study: Energy monitoring of a social housing building

2015 ◽  
Vol 103 ◽  
pp. 338-351 ◽  
Author(s):  
Llorenç Burgas ◽  
Joaquim Melendez ◽  
Joan Colomer ◽  
Joaquim Massana ◽  
Carles Pous
Keyword(s):  
Social Housing ◽  
Energy Monitoring ◽  
Multivariate Statistical ◽  
Statistical Monitoring

Multivariate statistical monitoring of an industrial SBR process. Soft-sensor for production and rubber quality

2011 ◽  
Vol 107 (2) ◽  
pp. 258-268 ◽  
Author(s):  
José L. Godoy ◽  
Roque J. Minari ◽  
Jorge R. Vega ◽  
Jacinto L. Marchetti
Keyword(s):  
Soft Sensor ◽  
Multivariate Statistical ◽  
Statistical Monitoring

Multivariate statistical monitoring of the aluminium smelting process

2011 ◽  
Vol 35 (11) ◽  
pp. 2457-2468 ◽  
Author(s):  
Nazatul Aini Abd Majid ◽  
Mark P. Taylor ◽  
John J.J. Chen ◽  
Brent R. Young
Keyword(s):  
Smelting Process ◽  
Multivariate Statistical ◽  
Statistical Monitoring ◽  
Aluminium Smelting

scholarly journals Fault Identification Based on Local Feature Correlation

2022 ◽  
pp. 119-146
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Data Processing ◽  
High Dimension ◽  
Dimensional Space ◽  
Original Data ◽  
Mapping Method ◽  
Kernel Functions ◽  
High Dimensional ◽  
Monitoring Methods ◽  
Multivariate Statistical ◽  
Low Dimensional

AbstractIndustrial data variables show obvious high dimension and strong nonlinear correlation. Traditional multivariate statistical monitoring methods, such as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space through appropriate kernel functions so as to achieve the goal of linear separability in the new space. However, the space projection from the low dimension to the high dimension is contradictory to the actual requirement of dimensionality reduction of the data. So kernel-based method inevitably increases the complexity of data processing.

Multivariate statistical monitoring of photovoltaic plant operation

2020 ◽  
Vol 205 ◽  
pp. 112317 ◽  
Author(s):  
Bilal Taghezouit ◽  
Fouzi Harrou ◽  
Ying Sun ◽  
Amar Hadj Arab ◽  
Cherif Larbes
Keyword(s):  
Multivariate Statistical ◽  
Plant Operation ◽  
Statistical Monitoring ◽  
Photovoltaic Plant
Sign in / Sign up

Export Citation Format

Share Document