XMILE - An Expert System for Maintenance Learning from Textual Reports (S)

Máximo, Eduardo; Pinheiro, Vládia

doi:10.18293/seke2018-197

Cited by 1 publication

(1 citation statement)

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“…This approach tries to extract meaningful information from the semi-structured text and raw notes provided by maintenance operators using Natural Language Processing (NLP) techniques. To the best of our knowledge none of the existing research related to NLP aims at detecting failures related to a wind turbine, rather they just identify technology trends [10] [11] In [12] the authors present a strategy using both monitoring and historical data to optimize maintenance, trying to predict the failures in order both to plan the interventions of maintenance team as well as the need of spare parts.…”

Section: Introductionmentioning

confidence: 99%

Pick-up & Deliver in Maintenance Management of Renewable Energy Power Plants

Carchiolo

Francesco²,

Longheu

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient. A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost. Wind farm maintenance presents, in fact, significant logistical challenges. They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself. In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms. In particular a pickup-anddelivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs. The solution was applied to a case study in a renewable energy power plant. Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

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Section: Introductionmentioning

confidence: 99%