Utilize este identificador para referenciar este registo: https://hdl.handle.net/10316/108332
Título: Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis
Autor: Reis, Marco S. 
Gins, Geert
Palavras-chave: industrial process monitoring; fault detection and diagnosis; prognosis; process health; equipment health
Data: 2017
Editora: MDPI
Projeto: project 016658 (references PTDC/QEQ-EPS/1323/2014, POCI-01-0145-FEDER-016658) financed by Project 3599-PPCDT (Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáticas) and co-financed by the European Union’s FEDER 
Título da revista, periódico, livro ou evento: Processes
Volume: 5
Número: 3
Resumo: 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.
URI: https://hdl.handle.net/10316/108332
ISSN: 2227-9717
DOI: 10.3390/pr5030035
Direitos: openAccess
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