Semi-supervised fault diagnosis of gearbox based on feature pre-extraction mechanism and improved generative adversarial networks under limited labeled samples and noise environment

Gearboxes are the most widely used component to transfer speed and power in many industries, and high precision gearbox fault diagnosis (FD) is pretty crucial for ensuring the safe operation of the machine. However, traditional FD methods often need a great quantity of labeled data, and are prone to noise interference in practical work, resulting in a relatively low diagnosis accuracy. With the intention of overcoming these problems, this paper proposes a semi-supervised FD approach based on feature pre-extraction mechanism and improved generative adversarial network (IGAN). First, the data is preprocessed by the feature pre-extraction mechanism based on wavelet transform. Then, limited labeled samples and a large number of unlabeled samples are sent to the IGAN model. Finally, two typical gearbox fault datasets are utilized to evaluate the feasibility and effectiveness of the proposed approach in limited labeled samples and noise environment. Trial results denote that the proposed approach has better diagnosis accuracy and anti-noise robustness than other approaches.

Automated summary

This paper proposes a semi-supervised fault diagnosis approach based on feature pre-extraction and improved generative adversarial network (IGAN) to address the issues of requiring a large amount of labeled data and noise interference in traditional methods. Experimental results demonstrate that the proposed approach achieves better diagnosis accuracy and anti-noise robustness in limited labeled samples and noise environment.