Process-Quality Monitoring Using Semi-supervised Probability Latent Variable Regression Models

2014 ◽  
Vol 47 (3) ◽  
pp. 8272-8277 ◽  
Author(s):  
Le Zhou ◽  
Zhihuan Song ◽  
Junghui Chen ◽  
Zhiqiang Ge ◽  
Zhao Li
Keyword(s):  
Latent Variable ◽  
Regression Models ◽  
Process Quality ◽  
Quality Monitoring ◽  
Latent Variable Regression

Probabilistic latent variable regression model for process-quality monitoring

2014 ◽  
Vol 116 ◽  
pp. 296-305 ◽  
Author(s):  
Le Zhou ◽  
Junghui Chen ◽  
Zhihuan Song ◽  
Zhiqiang Ge ◽  
Aimin Miao
Keyword(s):  
Regression Model ◽  
Latent Variable ◽  
Process Quality ◽  
Quality Monitoring ◽  
Latent Variable Regression

scholarly journals Multiscale Latent Variable Regression

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed N. Nounou ◽  
Hazem N. Nounou
Keyword(s):  
Latent Variable ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Multiple Scales ◽  
Principal Component Regression ◽  
Principal Component ◽  
Measurement Noise ◽  
Process Data ◽  
Latent Variable Regression ◽  
Modeling Algorithm

Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regression models at multiple scales using the scaled signal approximations of the data and then using cross-validation, and select among all MSLVR models the model which best describes the process. The main advantage of the MSLVR modeling algorithm is that it inherently accounts for the presence of measurement noise in the data by the application of the low-pass filters used in multiscale decomposition, which in turn improves the model robustness to measurement noise and enhances its prediction accuracy. The advantages of the developed MSLVR modeling algorithm are demonstrated using a simulated inferential model which predicts the distillate composition from measurements of some of the trays' temperatures.

Variable importance in latent variable regression models

2014 ◽  
Vol 28 (8) ◽  
pp. 615-622 ◽  
Author(s):  
Olav M. Kvalheim ◽  
Reidar Arneberg ◽  
Olav Bleie ◽  
Tarja Rajalahti ◽  
Age K. Smilde ◽  
...  
Keyword(s):  
Latent Variable ◽  
Regression Models ◽  
Variable Importance ◽  
Latent Variable Regression
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