Smart expert system improves repair decisions for turbine components
Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process
Artificial IntelligenceMachine Learning
Summary
Repairing parts in machines like gas turbines helps them last longer and saves resources compared to making new parts. The authors created a Smart Expert System that uses machine learning to help engineers decide the best way to repair these parts. They tested this system using real data from a gas turbine company and found that it works well to guide repair decisions and is stable to use. This helps make repairs more cost-effective and better for the environment.
What this means in practice
- •For industrial maintenance teams: Improve repair decisions and extend component life for gas turbines using machine learning-driven expert advice.
- •For automotive repair managers: Adopt scalable machine learning support systems to optimize repair choices in vehicle maintenance cycles.
Authors
Houkun Zhu, Helena Ebel, Dominik Scheinert, Florian Schmidt, Jens Altenkirch, Odej Kao
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.