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Open AccessArticle

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

by Marco S. Reis 1,* and Geert Gins 2
1
CIEPQPF-Department of Chemical Engineering, University of Coimbra Polo II, Rua Sílvio Lima 3030-790, Coimbra, Portugal
2
AIXIAL Belgium, Charleroise Steenweg 112, B-1060 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Leo H. Chiang and Richard D. Braatz
Processes 2017, 5(3), 35; https://doi.org/10.3390/pr5030035
Received: 1 June 2017 / Revised: 25 June 2017 / Accepted: 27 June 2017 / Published: 30 June 2017
(This article belongs to the Collection Process Data Analytics)
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos. View Full-Text
Keywords: industrial process monitoring; fault detection and diagnosis; prognosis; process health; equipment health industrial process monitoring; fault detection and diagnosis; prognosis; process health; equipment health
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Reis, M.S.; Gins, G. Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis. Processes 2017, 5, 35.

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