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

Yunsheng Su, Luojie Shi, Kai Zhou, Guangxing Bai and Zequn Wang

Reliability Engineering and System Safety, 2024, vol. 244, issue C

Abstract: 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.

Keywords: Fault diagnosis; Network optimization; Knowledge-informed deep learning; Convolutional neural network; Constrained Gaussian process (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:244:y:2024:i:c:s0951832023007779

DOI: 10.1016/j.ress.2023.109863

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