Maintenance management of compressed air systems through digital twin integration

This study addresses the issue of the underrepresentation of Compressed Air Systems (CAS) in maintenance research and proposes a digital twin-based dashboard specifically designed to meet the operational and diagnostic needs of these systems. Although CASs are indispensable auxiliary systems in industrial production, maintenance approaches remain reactive and lack data-driven frameworks, often defaulting to predefined manufacturer procedures rather than real-time diagnostics. Through semi-structured interviews conducted with maintenance managers from 20 factories across five different industrial sectors, key challenges were identified, including inconsistent record-keeping, limited internal expertise, and the absence of trend-based decision-making. In response to these issues, a digital twin-integrated dashboard was developed, combining real-time sensor data, historical failure records, and a 3D CAS system model to enhance predictive maintenance, failure analysis, and performance benchmarking. The system enables evidence-based maintenance planning, supports the evaluation of internal and external resources, and, with user permission, provides anonymized data access to manufacturers, facilitating continuous improvement and benchmarking processes. The proposed solution contributes to smart maintenance practices by offering a scalable, modular, and user-friendly tool that supports the digital transformation of CAS maintenance and strengthens decision-making in terms of reliability, efficiency, and cost-effectiveness.