Fault diagnosis of fan gearboxes based on EEMD energy entropy and SOM neural networks Online publication date: Fri, 06-Mar-2020
by Biao Ma; Gang Li; Guping Zheng; Weifeng Xu
International Journal of Information and Communication Technology (IJICT), Vol. 16, No. 2, 2020
Abstract: Aiming at the difficulty of feature extraction for gear fault diagnosis and the problem of traditional classification methods cannot diagnose the faults in wind turbine gearboxes adaptively, a new fault diagnosis method based on ensemble empirical mode decomposition (EEMD) energy entropy and SOM neural networks (SOM-NN) is proposed. Firstly, the EEMD method is used to decompose the original vibration signal of the gear under all kinds of condition into several intrinsic mode functions (IMF) and calculate the energy value of each IMF and the energy entropy of the signal. Then the IMF energy proportion and the signal energy entropy are selected to form a set of features which can reflect the fault vibration signal. The values of these features are inputted to SOM neural network for classification. The numerical simulation results show that the accuracy of the method is 100% in the fault diagnosis of wind turbine gearbox.
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