Journal Article

A Multi-Data Fusion-Based Bearing Load Prediction Model for Elastically Supported Shafting Systems

Shi, Liang, Cui, Liangzhong, Zheng, Ziling

2026

Overview

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems.

• Publisher: MDPI AG
• Subject: Accuracy / bearing load prediction / Boundary conditions / Datasets / Engineering / Fault diagnosis / Learning strategies / Machine learning / multi-sub-region modeling / Neural networks / Simulation / Simulation methods / small-sample learning / stacking ensemble learning / transfer learning