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 small sample due to the lack of fault signals. To solve this problem, a small sample focused intelligent fault diagnosis method via multi-modules gradient penalized generative adversarial networks (MGPGAN) is proposed. The proposed method consists of three network modules: Generator, Discriminator, and Classifier. By adversarial training, 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. 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 classification accuracy under the small sample condition.

Data-driven intelligent diagnosis models expect to mine the health information of machines from massive monitoring data. However, the size of faulty monitoring data collected in engineering scenarios is limited, which leads to few-shot fault diagnosis as a valuable research point. Fortunately, it is possible to reduce the required amount of training data by integrating prior diagnosis knowledge into diagnosis models. Inspired by this, we present a prior knowledge-augmented self-supervised feature learning framework for few-shot fault diagnosis. In the framework, 24 signal feature indicators are built to form prior features set based on existing diagnosis knowledge. Besides, a convolutional autoencoder is used to mine the general features, which are considered to potentially contain fault information that prior features do not possess. We design a self-supervised learning scheme for training the diagnosis model, which enables the model to learn both prior and general features served as proxy labels. As a result, the model is expected to mine richer features from limited monitoring data. The effectiveness of the proposed framework is verified using two mechanical fault simulation experiments. From the angle of prior diagnosis knowledge, the proposed framework provides a new perspective to the problem of few-shot intelligent diagnosis of machines.