Fault Diagnosis of Hydroelectric Units Based on IMF-MFDE and GRU
In response to the characteristics of non-stationary,nonlinearity,and strong noise in the vibration signals of hydroelectric units,a fault diagnosis method combining IMF multi-scale fluctuation dispersion entropy(MFDE)and gated cyclic unit(GRU)is proposed.Firstly,the jumping spider optimization algorithm(JSOA)is used to optimize the parameters of variational mode decomposition(VMD)for achieving the optimal decomposition and noise reduction effect of vibration signals.Secondly,the eigenmode function(IMF)obtained from the decomposition and noise reduction is re-constructed,and the multi-scale fluctuation dispersion entropy(MFDE)of the effective IMF is calculated as the fault fea-ture vector.Finally,a fault identifier for hydroelectric units is established by choosing feature vectors as the input of GRU.Taking the actual fault sample data of hydroelectric power plant units as an example,the fault recognition rate reached 97.83%,verifying the effectiveness of the proposed method.
vibration signal of hydroelectric unitfault diagnosisJSOAVMDMFDE
NETL
NSTL
万方数据
维普
