TY  - BOOK
AU  - Harrou, Fouzi
TI  - Statistical process monitoring using advanced data-driven and deep learning approaches: : theory and practical applications
SN  - 9780128193655
U1  - 629.895 
PY  - 2021///
CY  - Cambridge
PB  - Elsevier 
KW  - Machine learning
KW  - Multivariate analysis--Data processing
KW  - Process control--Statistical methods
N1  - Table of content

1. Introduction

2. Linear Latent Variable Regression (LVR)-Based Process Monitoring

3. Fault Isolation

4. Nonlinear latent variable regression methods

5. Multiscale latent variable regression-based process monitoring methods

6. Unsupervised deep learning-based process monitoring methods

7. Unsupervised recurrent deep learning schemes for process monitoring

8. Case studies

9. Conclusions and future perspectives

N2  - Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems
ER  -