Generalized extremal optimization of predictive maintenance to enhance monitoring of large experimental systems |
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| Pasquale Arpaia, Mario Girone, Domenico Maisto, Carlo Manna, Marco Pezzetti |
- Abstract:
- Predictive maintenance scheduling is an optimization problem aimed at defining the best activity sequence to minimize the expected cost over a time horizon. For very-large systems such as in experimental physics, maintenance optimization turns out to be very difficult owing to analytically intractable objective functions. In this paper, a meta-heuristic predictive maintenance algorithm based on the Generalized Extremal Optimization (GEO) is presented. With respect to state-of-the-art meta-heuristic techniques, the GEObased maintenance algorithm allows optimization procedure to be configured easily through only one parameter without a numerous population. Preliminary results of the algorithm performance validation on the liquid helium storage system of the Large Hadron Collider at CERN are reported.
- Keywords:
- Predictive maintenance; scheduling algorithms; Generalized Extremal Optimization; condition monitoring
- Download:
- IMEKO-TC4-2014-458.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC4
- Event name:
- TC4 Symposium 2014
- Title:
20th IMEKO TC4 Symposium on Measurements of Electrical Quantities (together with 18th TC4 International Workshop on ADC and DCA Modeling and Testing, IWADC)
"Research on Electrical and Electronic Measurement for the Economic Upturn"- Place:
- Benevento, ITALY
- Time:
- 15 September 2014 - 17 September 2014