Design of intelligent monitoring system for hydraulic turbine faults with improved K-proximity calculation and improved K-proximity algorithm
In response to the low accuracy of fault diagnosis and early warning of large water turbine bearings,which leads to poor monitoring and operation management of pumped storage power stations,a large water turbine bearing lubricating oil online monitoring system was designed.The eddy current sensor was used to collect the bear-ing oil data,and the improved K-nearest neighbor algorithm was used to accurately classify and diagnose the bear-ing faults.The results showed that the maximum similarity between the new fault and the fault identification ball in ensemble A was 0.478 7,which was lower than the similarity matching threshold of 0.6,indicating that the im-proved KNN algorithm can achieve accurate identification of new fault types,and has certain adaptability and scal-ability.The practical application further proves that the algorithm can meet the requirements of condition monitor-ing,fault diagnosis and early warning of hydraulic turbine bearings,and realize accurate monitoring and intelligent operation and maintenance management of hydropower stations.
万方数据
维普
