Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications

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.

Uses a data-driven based approach to fault detection and attributionProvides an in-depth understanding of fault detection and attribution in complex and multivariate systemsFamiliarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methodsIncludes case studies and comparison of different methods

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