Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenarios
Jiaxian Chen, Kairu Wen, Jingyan Xia, Ruyi Huang, Zhuyun Chen, Weihua Li
Abstract
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.