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Maintenance Planning Using Condition Monitoring Data

Daniel Olivotti (), Jens Passlick, Sonja Dreyer, Benedikt Lebek and Michael Breitner ()
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Daniel Olivotti: Leibniz Universität Hannover
Jens Passlick: Leibniz Universität Hannover
Sonja Dreyer: Leibniz Universität Hannover
Benedikt Lebek: BHN Dienstleistungs GmbH & Co. KG

A chapter in Operations Research Proceedings 2017, 2018, pp 543-548 from Springer

Abstract: Abstract Maintenance activities of machines in the manufacturing industry are essential to keep machine availability as high as possible. A breakdown of a single machine can lead to a complete production stop. Maintenance is traditionally performed by predefined maintenance specifications of the machine manufacturers. With the help of condition- based maintenance, maintenance intervals can be optimized due to detailed knowledge through sensor data. This results in an adapted maintenance schedule where machines are only maintained when necessary. Apart from time savings, this also reduces costs. An decision support system with optimization model for maintenance planning is developed considering the right balance between the probabilities of failure of the machines and the potential breakdown costs. The current conditions of the machines are used to forecast the necessary maintenance activities for several periods. The decision support system helps maintenance planners to choose their decision-making horizon flexibly.

Keywords: Predictive maintenance; Condition-based maintenance; Condition monitoring; Machine availability; Sensor data (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-89920-6_72

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