Deviations between the design and actual shafting occur due to limitations in ship construction accuracy. Consequently, accurately obtaining the relationship between the actual shafting load and displacement relationship based on the design shafting becomes challenging, leading to inaccurate solutions for bearing displacement values and low alignment efficiency. In this research article, to address the issue of incomplete actual shafting data, a transfer learning-based method is proposed for accurate calculation of bearing displacement values. By combining simulated data from the design shafting with measured data generated during the adjustment process of the actual shafting, higher accuracy can be achieved in calculating bearing displacement values. This research utilizes a certain shafting as an example to carry out the application of the bearing displacement value calculation method. The results show that even under the action of shafting deviation, the actual shafting load and displacement relationship model can become more and more accurate with the shafting adjustment process, and the accuracy of bearing displacement values calculation becomes higher and higher. This method contributes to obtaining precise shafting adjustment schemes, thereby enhancing alignment quality and efficiency of ship shafting.

The current shafting alignment process primarily relies on manual adjustments, resulting in low efficiency and limited precision. This study designs an automatic alignment system for multi-support shafting to improve alignment efficiency and accuracy, advancing the development of intelligent systems. The overall design of the automatic alignment system is proposed, including the establishment of shafting and actuator models. The alignment performance of two control strategies under different shafting deviations is validated through simulations. The results indicate that both the full-state feedback control strategy and the model predictive control strategy can regulate bearing load errors to within 1%. Compared to manual alignment, both strategies demonstrate significantly higher efficiency. The proposed automatic alignment system is feasible and lays a foundation for the development of high-precision shafting alignment systems.

This paper addresses the issue of insufficient accuracy and efficiency in existing methods for the alignment of marine propulsion systems with single stern tube bearing, caused by uncertainties in the actual parameters of propulsion systems and the scarcity of data, and a new alignment method based on fine-tuning a pre-trained model is proposed. First, a characterization method for the attitude of the main engine is proposed, taking into account the specific alignment requirements of marine propulsion systems with single stern tube bearing. Next, a pre-trained model is constructed based on large-scale samples from the design propulsion system and the fine-tuning of the pre-trained model is performed using small samples from the actual propulsion system’s alignment process to obtain the target model, which guides the practical alignment. Finally, the effectiveness and superiority of the proposed method are validated by applying actual measured data and by applying finite element simulations; in eight alignments, all verification parameter errors are much smaller than the maximum allowable error. The results show that the proposed method significantly improves the accuracy and efficiency of alignment of this type of propulsion system and provides a technical approach to the small-sample modeling problem in the alignment of propulsion systems.

To improve the accuracy of bearing load measurement in propulsion shafting, a bearing load measurement method based on redundant strain and jacking data fusion is proposed. Firstly, to address the issue of low measurement accuracy in strain gauge methods (SGM), a redundant strain gauge method (RSGM) is introduced to enhance the precision of strain-based measurements. Secondly, synchronize the measurement of strain data during the jacking phase and fuse the measurement data from the jacking phase based on the Bayesian estimation method to enhance the measurement accuracy of the jack-up method (JM). Lastly, the pre-decision results of the two types of measurement data are integrated at the decision level to obtain the final measured load. Simulation experiments have verified the correctness and applicability of the proposed measurement method. The research results indicate that, compared to the SGM, the RSGM offers higher measurement precision; the fusion measurement method can achieve more accurate results than the JM and RSGM under different bearing support conditions and jack-up scenarios.