Title: Intelligent sensor impact on predictive maintenance program costs

Authors: Soukaina Sadiki; Maurizio Faccio; Mohamed Ramadany; Driss Amegouz; Said Boutahari

Addresses: Laboratory for Energy and Sustainable Development, Higher School of Technology of Fez, Morocco ' Department of Innovation in Mechanics and Management, University of Padua, Via Venezia1, 35131 Padua, Italy ' Department of Innovation in Mechanics and Management, University of Padua, Via Venezia1, 35131 Padua, Italy ' Laboratory for Energy and Sustainable Development, Higher School of Technology of Fez, Morocco ' Laboratory for Energy and Sustainable Development, Higher School of Technology of Fez, Morocco

Abstract: In this work, we develop a simulation study based on economic optimisation to compare the economical impact of two maintenance policies, traditional failure maintenance policy with predictive maintenance policy that utilises intelligent network sensor information. The simulation study established in this work compare tow maintenance strategies: predictive maintenance and failure-based maintenance, in order to compare when it is less expensive to maintain the equipment before it breaks down using intelligent network sensors than to replace it after its breakdown, to sum up, if it is profitable to implement this new technology. Also with this proposed approach, the decision maker could be in the position to decide on a most appropriate economical framework for the optimum cost, based on the comparison between breakdown cost and the cost of sensors. The method can be used by companies to make a decision when considering implementing remote monitoring. To illustrate the use and the advantages of the proposed maintenance policy, a numerical example is investigated.

Keywords: predictive maintenance; failure-based maintenance; intelligent network sensors; downtime; cost optimisation; decision-making.

DOI: 10.1504/IJMOR.2020.10024616

International Journal of Mathematics in Operational Research, 2020 Vol.17 No.2, pp.170 - 185

Accepted: 09 Jun 2019
Published online: 21 Sep 2020 *

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