Abstract Details
80 / 2025-05-14 20:06:51
Wear Prediction of Engine Sliding Bearings Using Physics-Informed Neural Networks
WearPrediction,Physics-Informed Neural Networks,Equipment Health Management,Interpretability
Final Paper
lures, and accurately predicting the wear between
friction pairs is essential for equipment health management.
As diesel engines progressively develop toward higher explosion
pressures and power densities, the working environments of key
components such as crankshaft bearings become increasingly
harsh, making them more prone to wear issues that com
promise engine reliability. Most existing data-driven methods
primarily focus on wear modeling using sparse data, yet such
approaches exhibit limitations in interpretability. To address
this challenge, this paper proposes a physics-informed neural
network approach by integrating the Archard wear law and
typical wear processes as physical constraints into the neural
network framework, thereby enhancing model interpretability.
Experimental results demonstrate that incorporating prior
physical knowledge improves the overall performance of the
neural network. Furthermore, parameter sensitivity analysis
reveals that the model maintains behavior consistent with
typical wear processes even when applied to unseen data.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
-
2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
