A Small Sample Focused Intelligent Fault Diagnosis Scheme of Machines via Multimodules Learning With Gradient Penalized Generative Adversarial Networks

Abstract

Intelligent fault diagnosis of machines has long been a research hotspot and has achieved fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small sample due to the lack of fault signals. To solve this problem, a small sample focused intelligent fault diagnosis method via multimodules gradient penalized generative adversarial networks is proposed. The proposed method consists of three network modules: generator, discriminator, and classifier. By adversarial training, the generator can generate mechanical signals in different health conditions. Because of the high similarity to the signals obtained in practice, the generated signals can also be used as training data so that the limited training dataset of the proposed method is expanded. The classifier has a strong ability to extract fault features from raw mechanical signals and then classify different health conditions. The experimental results on two bearing vibration datasets indicate that the proposed method can not only generate bearing vibration signals but also obtain fairly high fault classificati on accuracy under the small sample condition.