Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
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Type
E-Book
ISBN 10
0128193654
ISBN 13
9780128193655
Category
Guidance and Counseling
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Publication Year
2021
Publisher
Elsevier, United States
Pages
328 / 322
Subject
Statistics, Process control -- Statistical methods, Multivariate analysis -- Data processing, Machine learning
Tags
Abstract
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. --
Description
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
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
Number of Copies
1
Library | Accession‎ No | Call No | Copy No | Edition | Location | Availability |
---|---|---|---|---|---|---|
Main | 204 | 1 | E-Library | Yes |