Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process

Abstract: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.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.33641 [cs.AI]

(or arXiv:2609.33641v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.33641 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/IEEM55944.2022.9989791 Focus to learn more DOI(s) linking to related resources

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