Dimension Reduction Method of Rotor Fault Dataset Based on MGCD
A rotor fault dataset dimensionality reduction algorithm based on multi graph collaborative decision making(MGCD)is proposed,in order to address the issues of difficulty in classifying fault datasets and low ac-curacy in fault pattern recognition due to high feature dimensions.This algorithm first builds on the framework of marginal Fisher analysis(MFA)algorithm to solve the problem of local inseparability of fault categories caused by a single graph structure,through establishing nearest neighbor graphs and far neighbor graphs.Secondly,it uses the maximum divergence weighted difference method to try to weaken the impact of small sample prob-lems.The performance of the algorithm is verified using two different structural types of rotor system fault simu-lation datasets.The results show that the sensitive fault dataset obtained by using this algorithm to reduce the di-mensionality of the fault dataset,can make the differences between fault categories more prominent,thereby im-proving the accuracy of fault pattern recognition.This study can provide a certain research reference for improv-ing the level of intelligent fault diagnosis technology in rotating machinery.
NETL
NSTL
万方数据
维普
