Open AccessPREDICTIVE MAINTENANCE THROUGH DATA-DRIVEN APPROACHES
Author(s) -
Jorge Augusto Meira,
Eugenia Pérez Pons,
Javier Parra-Domínguez,
Goreti Marreiros,
Carlos Ramos
Publication year - 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.14201/0aq03111326
Subject(s) - industry 4.0 , industrial revolution , predictive maintenance , computer science , big data , field (mathematics) , inefficiency , the internet , industrial internet , productivity , data science , manufacturing engineering , engineering management , internet of things , engineering , computer security , world wide web , data mining , mathematics , macroeconomics , political science , economics , pure mathematics , law , reliability engineering , microeconomics
Nowadays, the Industrial Internet promises to transform our world. The melding of the global industrial system that was made possible because of the Industrial Revolution, with the open computing and communication systems developed as part of the Internet Revolution, opens new frontiers to accelerate productivity, reduce inefficiency and waste, and enhance the human work experience. With the emergence of Industry 4.0, smart systems, machine learning within artificial intelligence, predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. This paper focus on the PdM field, describing and specifying, its techniques, applications in the real world, methods and approaches widely used as such its challenges.