###### Abstract  
To improve the accuracy of bearing load measurement in propulsion shafing, 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. b 20  
as piston leakage. In addition to this, the calculation process of the JM also involves the use of a correction factor. These coefficients are derived from theoretical calculations and may deviate from actual values. [PERSON] et al. [9] used curve fitting methods to determine the bearing loads, thus avoiding errors associated with the use of correction factor in the calculation process. Compared to the JM, the SGM is more complex to implement and has higher requirements for the process, with measurement accuracy being more significantly affected by human and environmental factors [5]. To improve the measurement accuracy of the SGM, [PERSON] [10] has, in detail, deduced the impact of different strain gauge placement positions on the calculation results of the SGM and theoretically derived a general formula to reduce the errors in the SGM. In addition to this, the selection of bearing support points can also greatly affect the accuracy of the SGM's measurement results. [PERSON] et al. [11] have analyzed the impact of the assumed support point error and bending moment error in the SGM calculation on the measurement results and have introduced a strain correction coefficient to improve the load identification accuracy. Multi-sensor data fusion technology can improve the reliability of measurement data, suppress negative environmental factors included in the data, eliminate redundant data, and enhance confidence in measurement results [12; 13; 14]. For the SGM, employing redundant strain design can improve the accuracy of strain gauge measurement results. [PERSON] et al. [15] have utilized the D-S evidence theory for the fusion of redundant strain data, which has led to improved accuracy in damage identification. The fusion of jacking data and strain data belongs to heterogeneous data fusion, where the differences between heterogeneous data are generally large, and decision-level fusion is commonly adopted [16]. [PERSON] et al. [17] used a weighted fusion method for decision-level fusion of heterogeneous radar data to improve the estimation quality of ship berthing parameters. In summary, the existing literature has conducted research on enhancing measurement accuracy using either the JM or the SGM, with the improvement in precision being constrained by the measurement principles and processes inherent to each method. This paper innovatively applies multi-sensor data fusion technology to bearing load measurement, combining the respective strengths of the JM and SGM. By considering the interrelation of the measurement processes of these two methods, the measurement data from the processes are subjected to multiple rounds of deep integration, thereby effectively enhancing measurement accuracy under equivalent measurement process conditions. The contents of the paper are arranged as follows: Section 2 introduces the bearing load measurement method based on multi-sensor data fusion technology.