THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE - CHEN XiaoYue; GE Dang; HUANG JiangPing; LIU Xiong; ZHANG XiaoYan (Jixie qiangdu)
For the faults of hydropower unit with strong coupling and uncertainty,the traditional fault expression method based on the uniform feature vector is easy to submerge the effect of fault expression and limit the fault diagnosis accuracy and timeliness directly.Given this,we presented a bionic fault diagnosis method,which imitates the human brain thinking and proposes the concept of dependent feature vector（DFV）,which logical structure is described by feature selection tree and extracted by feature extraction tree.DFV deeply excavates the intricate relationship between faults by using its nested structure,and realizes the comprehensive mining of the quantity and quality differences between faults of hydroelectric units.It deeply reveals the intrinsic relationship between different faults and faults and features,effectively extracts the essential differences of different faults,and improves the validity of fault expression.Furthermore,the intelligent classification method is realized by combining DFV and probabilistic neural network,the experimental results show that the method can complete the fault diagnosis of hydropower units and achieve excellent diagnostic validity and timeliness.