Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines

Abstract

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.