Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenarios

The development of Internet of Things technology provides abundant data resources for prognostics health management of industrial machinery, and data-driven methods have shown their powerful ability in the field of fault diagnosis. However, these methods have several limitations: 1) Using less labeled data to obtain higher accuracy is a challenging task, which limits the application of diagnostic models in practical applications. 2) Physics-informed knowledge is largely ignored during the modeling process, which contains a wealth of information that can reflect the harmonic drive's health status. To address these challenges, a self-supervised fault diagnosis framework is developed by integrating prior knowledge with deep learning to improve the accuracy and reliability of diagnosis models in industrial applications. Specifically, the physics-based knowledge including 32-dimensional time domain, frequency domain, and time-frequency domain features, is first designed to provide fault information and significantly reduce the amount of data required for deep learning. Furthermore, a self-supervised knowledge embedded auto-encoder network is built by employing the prior knowledge in the multi-scale convolutional auto-encoder. With the ability to integrate prior knowledge and the self-supervised learning mechanism, the proposed method can provide a strong tool for knowledge representation and an effective solution for fault diagnosis under a few-shot industrial scenario. The experimental results conducted on a real harmonic drive fault dataset prove that the proposed network framework provides effective insights on fault diagnosis and has excellent generalizability in practical industrial applications.