Fault Diagnosis of Planetary Gearbox based on 1-DCNN - Xuanyi Xue; Xinyu Pang (Jixie chuandong)
Traditional machine learning methods have disadvantages such as low recognition rate and complicated feature extraction operations in the planetary gearbox fault diagnosis. In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep convolutional neural network （1-DCNN） is proposed， and the original signals are directly input to the network for diagnosis. The accuracy of diagnosing five kinds of fault signals of planetary gear of planetary gear box can reach 100%， and the diagnostic accuracy and efficiency are better than other commonly used algorithms.