Unsupervised Gear Fault Diagnosis Using Raw Vibration Signal Based on Deep Learning

Abstract

Gears are the most common parts of a mechanical transmission system. Gear wearing faults could cause the transmission system to crash and give rise to the economic loss. It is always a challenging problem to diagnose the gear wearing condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear wearing fault with relatively few raw vibration signal data. This method is mainly based on the theory of wearing fault diagnosis, through creatively combining with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is verified by experiments of six types of gear wearing conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear wearing conditions and show the obvious trend according to the severity of gear wear faults. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.