Deep Learning-Based Decision Support System for Hydraulic Facilities Maintenance

This paper introduces a deep learning-based decision support system for hydraulic facilities maintenance. The system utilizes a CNN-LSTM hybrid model for fault prediction, combined with big data processing and intelligent decision algorithms to support preventive maintenance comprehensively. The system demonstrates excellent performance in fault prediction accuracy, data processing capability, and response time, meeting or exceeding design objectives. Continuous operational testing has proven the system's stability and reliability. While there is room for optimization in some modules, the system successfully applies artificial intelligence technology to the field of hydraulic facilities maintenance, offering innovative solutions to enhance maintenance efficiency, reduce operational costs, and extend equipment lifespan. With continued data accumulation and algorithm refinement, the system is expected to deliver greater value in practical applications, advancing the intelligence of hydraulic engineering management.