Maintenance Required
Maintenance optimization is a rapidly evolving field focused on improving efficiency, reducing costs, and enhancing safety across diverse sectors. Current research emphasizes the use of machine learning, including deep neural networks, ensemble classifiers, and logistic regression, to predict equipment failures, automate diagnostic processes, and optimize maintenance schedules based on historical data and real-time sensor information. These advancements are significantly impacting various industries, from transportation and infrastructure to healthcare and manufacturing, by enabling proactive maintenance strategies and reducing downtime. The development of robust ontologies and standardized data frameworks further supports the integration and sharing of maintenance-related knowledge.
Papers
FMEA Builder: Expert Guided Text Generation for Equipment Maintenance
Karol Lynch, Fabio Lorenzi, John Sheehan, Duygu Kabakci-Zorlu, Bradley Eck
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG
Laifa Tao, Qixuan Huang, Xianjun Wu, Weiwei Zhang, Yunlong Wu, Bin Li, Chen Lu, Xingshuo Hai
Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)
Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede
Augmenting train maintenance technicians with automated incident diagnostic suggestions
Georges Tod, Jean Bruggeman, Evert Bevernage, Pieter Moelans, Walter Eeckhout, Jean-Luc Glineur