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

Fouzi Harrou, Ying Sun, Amanda Hering, Muddu Madakyaru, Abdelkader Dairi

Research output: Book/ReportBookpeer-review

53 Scopus citations

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.

Original languageEnglish (US)
PublisherElsevier
Number of pages315
ISBN (Electronic)9780128193655
DOIs
StatePublished - Jan 1 2020

ASJC Scopus subject areas

  • General Engineering
  • General Chemical Engineering

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