Knowledge-informed deep networks for robust fault diagnosis of rolling bearings

Effective fault defection is of critical importance in condition-based maintenance to improve the reliability of engineered systems and reduce operational cost. This paper introduces a knowledge-informed deep learning approach to fuse prior knowledge and critical health information extracted from raw monitoring data for robust fault diagnosis of rolling bearings. A set of knowledge-based features is first extracted based on prior knowledge of engineered systems. A knowledge-informed deep network (KIDN) is then designed to leverage these knowledge-based features with data-driven machine learning for the accurate prediction of bearing faults. To further enhance the generalizability of deep networks for fault diagnosis and alleviate extensive tuning efforts, a novel generalizability-based adaptive network design strategy is developed based on constrained Gaussian process (CGP) to quickly obtain the promising architectures for the development of knowledge-informed deep networks. Specifically, it involves the training of a constrained Gaussian process (CGP) surrogate model to predict the generalizability of KIDN and seeking potential improvements by exploring alternative network architectures within a vast design space. Four experimental case studies are implemented to validate the proposed methodology.