Towards Intelligent Fault Diagnosis under Small Sample Condition via A Signals Augmented Semi-supervised Learning Framework

Recently, intelligent fault diagnosis has achieved fruitful research results. However, the small sample is still the major problem in fault diagnosis owing to lacking fault data of machines. In view of this, a signals augmented semi-supervised learning scheme is proposed for intelligent fault diagnosis in the case of small sample. In the proposed method, fault signal samples are generated by generative adversarial networks (GAN). The fault classifier is trained in a semi-supervised way using the generated samples and a small number of real samples. Besides, attention mechanism is applied in the fault classifier for sensitive feature extraction. The trained fault classifier is capable of accurate fault classification. Results indicate that the proposed method is effective in mechanical fault diagnosis under the small sample condition.