Predictive maintenance through data-driven approaches

  1. Jorge Meira 1
  2. Eugenia Pérez Pons 2
  3. Javier Parra Domínguez 2
  4. Goreti Marreiros 1
  5. Carlos Ramos 1
  1. 1 Institute of Engineering, Polytechnic of Porto
  2. 2 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Book:
Proceedings of the III Workshop on disruptive information and communication technologies for innovation and digital transformation
  1. Javier Parra Domínguez (ed. lit.)
  2. Sara Rodríguez González (ed. lit.)
  3. Javier Prieto Tejedor (ed. lit.)
  4. Juan Manuel Corchado Rodríguez (ed. lit.)

Publisher: Ediciones Universidad de Salamanca ; Universidad de Salamanca

ISBN: 978-84-1311-579-5

Year of publication: 2021

Pages: 13-25

Congress: Workshop on disruptive information and communication technologies for innovation and digital transformation (3. 2020. Salamanca)

Type: Conference paper

DOI: 10.14201/0AQ03111326 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

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 onthe PdM field, describing and specifying, its techniques, applications in thereal world, methods and approaches widely used as such its challenges.

Data source: Dialnet