The invention discloses a high-water-head sand-containing
water flow hydropower station water turbine wear shutdown fault prediction method and an intelligent
reporting system, and belongs to the field of hydroelectric power generation
equipment state monitoring and early warning. The method comprises the following steps: collecting multi-source heterogeneous data in real time and fusing the multi-source heterogeneous data; a
water turbine wear state comprehensive index is constructed, and the overall health degree of a
water turbine flow passage component is quantified; predicting the remaining service life based on a physical mechanism and a data-driven fusion model; and dividing and dynamically adjusting a fault
risk level, and generating adaptive early warning information. According to the method,
sediment characteristic monitoring, vibration
spectrum analysis, efficiency calculation and historical maintenance data are fully fused, risk grade evaluation and self-adaptive early warning of abrasion faults are achieved, the accuracy and the real-time performance of water
turbine state monitoring are improved, meanwhile, a decision support report is automatically generated through an intelligent reporting device, and the water
turbine state monitoring accuracy and the water
turbine state monitoring real-time performance are improved.
Power station operation and maintenance are promoted to be converted from regular maintenance to
predictive maintenance, and the method has good
engineering application value.