Distributed Predictive Maintenance Through Edge Computing

This study presents a framework for distributed predictive maintenance using the BaSyx platform and edge computing devices. By integrating predictive maintenance algorithms, the system can estimate remaining useful life, detect early faults, and identify anomalies without prior failure data. The real-time processing capabilities of edge devices, combined with a local server, aid in immediate data integration and decision-making to optimize maintenance scheduling and improve operational reliability. While vehicles are used as a specific example in this research, the proposed framework is adaptable and applicable to predictive maintenance across various industrial assets. This study provides a baseline for advanced predictive maintenance strategies.