A Concept of knowledge-based Recommendation System for Sustainable Maintenance in Flexible Manufacturing Systems

Abstract

Flexible manufacturing systems (FMS) face escalating challenges due to the vast number of components and potential failure causes, intensified by diverse product portfolios and fluctuating production volumes. This paper introduces an agent-based adaptive recommendation system that signifies a paradigm shift in data-driven maintenance management. By integrating the expertise of maintenance engineers and personnel, the system employs a Natural Language Processing (NLP)-based model to classify symptoms, causes, and solutions with high accuracy (achieving 100 % for symptoms, 90 % for causes, and 85 % for solutions). Leveraging Bayesian networks to model cause-and-effect relationships, the system enables probabilistic inferences and provides resource-efficient recommendations that minimize energy use, resource consumption, and waste. Continuous feedback mechanisms enhance the system's performance over time, improving diagnostic and repair processes while promoting sustainable maintenance practices.