Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance

Alexandros Bousdekis (), Babis Magoutas, Dimitris Apostolou and Gregoris Mentzas
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Alexandros Bousdekis: National Technical University of Athens (NTUA)
Babis Magoutas: National Technical University of Athens (NTUA)
Dimitris Apostolou: University of Piraeus
Gregoris Mentzas: National Technical University of Athens (NTUA)

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 6, No 9, 1303-1316

Abstract: Abstract In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.

Keywords: Condition based maintenance; Literature review; Decision tree learning; Decision support; Machine learning; Prognosis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-015-1179-5

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