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Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process — arXiv:2609.33641

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33641
License (as archived): CC0-1.0
Archived: 2026-10-07T20:57:00.347Z
SHA-256 of the archived text: 76268422ae74a656d7030123821c762ab337b5e7bd6339d0142c7e19d17751b9
Stable URL of this page: https://metraipolis.com/documento/dc_a75bb0897bb3

How to cite

Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process — arXiv:2609.33641 — arXiv (en), https://arxiv.org/abs/2609.33641 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a75bb0897bb3 (Metraipolis archive dc_a75bb0897bb3, sha256 76268422ae74a656d7030123821c762ab337b5e7bd6339d0142c7e19d17751b9)

BibTeX

@misc{dca75bb0897bb3,
  title = {Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process — arXiv:2609.33641},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33641},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a75bb0897bb3; sha256 76268422ae74a656d7030123821c762ab337b5e7bd6339d0142c7e19d17751b9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33641. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecycle Management (PLM), repairing components in frequent intervals can extend the lifetime expectation of the product, provide higher cost efficiency compared to newly produced components, and even improve the part design during the repair cycle. Another aspect of repair concerns sustainability, as products often contain rare materials. The emissions produced by the repair process are usually smaller than mining materials and casting new components. To optimize the repair process further, we propose the Smart Expert System (SES), which assists engineering experts with machine learning-based decision support throughout the repair process. We elaborate on its IT architecture and present machine learning models employed for representative MRO use cases. The SES is evaluated using actual industry data from a leading gas turbine company and demonstrably fulfills formulated requirements concerning the suitability of the overall decision support and the stability of the enclosing IT architecture.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.33641 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:01.319Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).