Statistical process monitoring using advanced data-driven and deep learning approaches: theory and practical applications
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9780128193655
- 629.895 HAR
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 629.895 HAR (Browse shelf(Opens below)) | 1 | Available | 004465 |
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621.3822 DEV Nonlinear blind source separation and blind mixture identification: | 625.725028563 KAN Artificial intelligence in highway location and alignment optimization: | 629.8924019 NAM Trust in human-robot interaction | 629.895 HAR Statistical process monitoring using advanced data-driven and deep learning approaches: | 630.208563 AHM Agriculture 5.0: | 650 URB Digitalization cases: how organizations rethink their business for the digital age | 650.0285 HOD Business analytics using R - a practical approach |
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
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.
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