Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
  • Author : Fouzi Harrou
  • Release Date : 03 July 2020
  • Publisher : Elsevier
  • Genre : Technology & Engineering
  • Pages : 328
  • ISBN 13 : 9780128193662

Download or read book entitled Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches written by Fouzi Harrou and published by Elsevier online. This book was released on 03 July 2020 with total pages 328. Available in PDF, EPUB and Kindle. Book excerpt: 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 attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Metal Additive Manufacturing

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Author : Ehsan Toyserkani,Dyuti Sarker,Osezua Obehi Ibhadode,Farzad Liravi,Paola Russo,Katayoon Taherkhani
Publisher : John Wiley & Sons
Genre : Science
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