Fault Diagnosis Analysis of Variable Working Condition Gearbox based on DWAE and GRUNN Combination Model

Abstract

In order to better identify the faults of gearbox under varying working conditions caused by noise and time-varying speed conditions， a fault identification method of gearbox under varying conditions is developed by combining DWAE and GRUNN， which could extract robust fault characteristics from noisy samples. The Adam and Dropout methods are used for training， and the softmax classifier is used to accurately identify the operating state of the gearbox under different working conditions. The results show that the six fault states of gears can be separated effectively when the model is used to identify gear faults， so as to meet the optimization function of gear state clustering. This model can extract the robust characteristic parameters of DWAE， and it can also use GRUNN to eliminate the gradient. When the number of training samples increased， the accuracy of waiting samples also improved significantly. When the number of samples is more than 200， stable accuracy could be obtained by testing the waiting samples， and the accuracy is the highest by dWAe-Grunn method. The model can maintain good accuracy under variable speed working conditions.

Keywords

• Variable working condition gearbox;Fault identification;Deep wavelet autoencoder （DWAE）;Gated cyclic unit neural network （GRUNN）;Accuracy