Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications 1st Edition Fouzi Harrou – eBook PDF
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications – Ebook PDF
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications – Ebook PDF Instant Delivery – ISBN(s): 9780128193655,0128193654
Product details:
- ISBN-10 : 0128193654
- ISBN-13 : 978-0128193655
- Author:
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.
Table contents:
Chapter 1 – Introduction
Chapter 2 – Linear latent variable regression (LVR)-based process monitoring
Chapter 3 – Fault isolation
Chapter 4 – Nonlinear latent variable regression methods
Chapter 5 – Multiscale latent variable regression-based process monitoring methods
Chapter 6 – Unsupervised deep learning-based process monitoring methods
Chapter 7 – Unsupervised recurrent deep learning scheme for process monitoring
Chapter 8 – Case studies
Chapter 9 – Conclusion and further research directions
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