A Novel Real-Time Fault Diagnosis Method for Planetary Gearbox Using Transferable Hidden Layer

Planetary gearbox with high speed and high precision is important for sophisticated power equipment, and the effective sensor data are necessary for reliability and stability. However, the traditional models for the fault diagnosis of planetary gearbox are difficult to maintain the high accuracy and effectiveness when the amount of training data are scarce. To solve this problem, inspired by deep learning, a novel intelligent method for planetary gearbox fault diagnosis is proposed by utilizing advantage of gated recurrent neural network (RNN) in the feature extraction. Moreover, the dropout technology is introduced to the proposed method to further reduce the requirement of training data. By dividing the parameters of the classification layer and using a few new fault data to fine-tune the learned network parameters, the proposed method can quickly realize the diagnosis of new type faults while maintaining the original recognition ability. Finally, the test signals of the planetary gearbox are used to verify the proposed method. The results show the proposed method has the advantages of high diagnostic accuracy, fast recognition speed, and well real-time in fault diagnosis. Furthermore, the proposed method can make full use of the less information to diagnose different types of faults under the different working conditions.