Abstract
Sliding bearings have become essential components in rotating machinery and are widely used in robotics field. Wear is the major failure mode of sliding bearings, while traditional prognostics methods for wear mostly rely on iterative calculations based on physical models, which are time-consuming, inefficient, and have limited practicality. This paper proposes a novel prognostics method for wear based on the Sequential Hybrid of Finite Element and Neural Network (SFENN), which exhibits high accuracy in wear prognostics and instantaneous output capability. The proposed method integrates finite element physical model and deep neural network through a sequential hybrid approach. In the offline phase, we establish the wear physical model of sliding bearing based on wear theory and wear test, then simulated wear data can be obtained through numerical simulations under different conditions. Deep neural network is designed and trained according to the characteristics of simulated data. After SFENN is trained, it can provide instantaneous prognostics of wear profile for new conditions of bearing, overcoming the limitations of traditional methods. The experiment validated the effectiveness of the proposed method, providing a further solution for the digital twin degradation model of mechanical components.
This work was supported by the National Key Research and Development Project of China [grant number 2020YFB1709103, 2018YFB1700604]; the Beijing Municipal Natural Science Foundation [grant number 3182012].
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Dai, J., Tian, L. (2023). A Novel Prognostic Method for Wear of Sliding Bearing Based on SFENN. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14275. Springer, Singapore. https://doi.org/10.1007/978-981-99-6504-5_19
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