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2 Polish Maritime Research

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A new six-DoF parallel mechanism for captive model test

100%

Lu Y. , Wu J. , Li W. , Wu Y.

2020

tom nr 3

4--15

In order to obtain the hydrodynamic coefficients that can save cost and meet the accuracy requirements, a new hydrodynamic test platform based on a 6DoF (six degrees of freedom) parallel mechanism is proposed in this paper. The test platform can drive the ship to move in six degrees of freedom. By using this experimental platform, the corresponding hydrodynamic coefficients can be measured. Firstly, the structure of the new device is introduced. The working principle of the model is deduced based on the mathematical model. Then the hydrodynamic coefficients of a test ship model of a KELC tank ship with a scale of 1:150 are measured and 8 typical hydrodynamic coefficients are obtained. Finally, the measured data are compared with the value of a real ship. The deviation is less than 10% which meets the technical requirements of the practical project. The efficiency of measuring the hydrodynamic coefficients of physical models of ships and offshore structures is improved by the device. The method of measuring the hydrodynamic coefficients by using the proposed platform provides a certain reference for predicting the hydrodynamic performance of ships and offshore structures.

Fault diagnosis of imbalance and misalignment in rotor-bearing systems using deep learning

88%

Liu F. , Li W. , Wu Y. , He Y. , Li T.

Polish Maritime Research

2024

tom nr 1

102--113

Rotor-bearing systems are important components of rotating machinery and transmission systems, and imbalance and misalignment are inevitable in such systems. At present, the main challenges faced by state-of-the-art fault diagnosis methods involve the extraction of fault features under strong background noise and the classification of different fault modes. In this paper, a fault diagnosis method based on an improved deep residual shrinkage network (IDRSN) is proposed with the aim of achieving end-to-end fault diagnosis of a rotor-bearing system. First, a method called wavelet threshold denoising and variational mode decomposition (WTD-VMD) is proposed, which can proces original noisy signals into intrinsic mode functions (IMFs) with a salient feature. These one-dimensional IMFs are then transformed into two-dimensional images using a Gramian angular field (GAF) to give datasets for the deep residual shrinkage network (DRSN), which can achieve high levels of accuracy under strong background noise. Finally, a comprehensive test platform for a rotor-bearing system is built to verify the effectiveness of the proposed method in the field. The true test accuracy of the model at a 95% confidence interval is found to range from 84.09% to 86.51%. The proposed model exhibits good robustness when dealing with noisy samples and gives the best classification results for fault diagnosis under misalignment, with a test accuracy of 100%. It also achieves a higher testing accuracy compared to fault diagnosis methods based on convolutional neural networks and deep residual networks without improvement. In summary, IDRSN has significant value for deep learning engineering applications involving the fault diagnosis of rotor-bearing systems.