Research on fault identification of circuit breaker components based on multi feature fusion and deep belief network
In order to standardize the fault identification and diagnosis of circuit breaker components,improve the stability of circuit breaker operation,multiple feature fusion technology and deep belief network are introduced to carry out the design and research of circuit breaker component fault identification methods.Simultaneously arrange sensors,establish channels between sensors and terminal computers,collect operating signals of circuit breaker components and deeply fuse multiple features;To eliminate vibration signals caused by other factors and achieve accurate identification of circuit breaker component faults,a deep belief network is introduced to reconstruct the vibration signals of circuit breaker components;Using the reconstructed micro adjustment signal as the core,identify and classify circuit breaker component faults through analysis of its time and frequency domains.The comparative experimental results show that the designed method can achieve accurate diagnosis of circuit breaker component faults and identification of fault types,and the application effect is good.
multi feature fusionsignal reconstructionfault identificationcircuit breakerdeep belief network
NETL
NSTL
万方数据
