A Study on the Intelligent Operation and Maintenance Optimisation of Engineering Systems Based on Deep Learning and Multi-source Data Fusion

Modern engineering systems operate in complex environments, and traditional operation and maintenance models can no longer meet the requirements for high reliability; therefore, it is necessary to explore intelligent degradation assessment mechanisms. To address the issue of low utilisation of massive amounts of heterogeneous monitoring data, a cross-modal information mapping approach has been established to adaptively extract underlying features. A domain-adaptive network architecture is employed to enhance generalisation capabilities for unknown operating conditions, and edge computing nodes are deployed for real-time processing. This effectively eliminates spatio-temporal alignment errors in the exchange of heterogeneous information, improves the accuracy of equipment condition prediction throughout the entire lifecycle, and provides a reliable theoretical foundation for decision-making to ensure the efficient and stable operation of industrial equipment.