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Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in scene reconstruction and novel view synthesis on benchmark datasets. However, real-world images are frequently affected by degradations such as camera shake, object motion, and lens defocus, which not only compromise image quality but also severely hinder the accuracy of 3D reconstruction—particularly in fine details. While existing deblurring approaches have made progress, most are limited to addressing a single type of blur, rendering them inadequate for complex scenarios involving multiple blur sources and resolution degradation. To address these challenges, we propose Gaussian Splatting with Multi-Scale Deblurring and Resolution Enhancement (GS-MSDR), a novel framework that seamlessly integrates multi-scale deblurring and resolution enhancement. At its core, our Multi-scale Adaptive Attention Network (MAAN) fuses multi-scale features to enhance image information, while the Multi-modal Context Adapter (MCA) and adaptive spatial pooling modules further refine feature representation, facilitating the recovery of fine details in degraded regions. Additionally, our Hierarchical Progressive Kernel Optimization (HPKO) method mitigates ambiguity and ensures precise detail reconstruction through layer-wise optimization. Extensive experiments demonstrate that GS-MSDR consistently outperforms state-of-the-art methods under diverse degraded scenarios, achieving superior deblurring, accurate 3D reconstruction, and efficient rendering within the 3DGS framework.

With the development of aerospace, military and other fields, higher requirements are put forward for the life, reliability and accuracy of rolling bearings. Rolling element slippage, as a phenomenon that cannot be ignored in the work of rolling bearings, affects the accuracy and life of rolling bearings. The present paper discusses the dynamic modelling of rolling bearing slippage, drawing on the research conducted by both domestic and international scholars in this field. The variations in rotational speed and revolution speed, the fluctuations in sliding speed between the inner ring raceway and outer ring raceway, as well as the impact of bearing load and speed on rolling element slippage during the process.

Ultrasonic pressure measurement is an advanced measurement technology, but it has a great influence on temperature. Aiming at the problem that the pressure of ultrasonic measurement pipeline is affected by temperature, the pipeline is modeled and simulated by finite element method. The ultrasonic transducer is used to transmit and receive sound waves. The time difference of sound waves received by the transducer under different temperatures is simulated to determine the corresponding sound velocity. The data is analyzed and processed to establish the mathematical model of sound velocity and temperature, The influence of temperature on pressure measurement is analyzed, which plays an important role in reducing the pressure error of ultrasonic measurement pipeline.

Vibration characteristics under unbalanced-misaligned coupling fault of propeller shaft were studied. Firstly, the rigid-flexible mixing model of propeller shaft system test bench is established by using Solidworks and ADAMS. Then, the shafting vibration characteristics in time and frequency domain are obtained by simulation analysis under normal, unbalanced, misaligned and unbalanced-misaligned coupling faults. Finally, the influence of rotational speed on unbalanced-misaligned coupling fault shaft system is analysed. The simulation study shows that the vibration spectrum of shafting under unbalanced-misaligned coupling fault is mainly the superposition of the spectrum under each single fault, and the vibration caused by each fault is different in different locations. The increase of speed will aggravate the vibration of shafting.

In the magnetic suspension bearing system, the protection bearing, as its key component, plays a role in supporting the safe operation of the drop rotor after the failure of the magnetic suspension bearing, to protect the whole system from damage. Based on a multi-body dynamics simulation, this paper studied the characteristics of the protection bearing, such as the speed fluctuation of the rotor and the bearing inner ring, and the contact force between the rotor and the protection bearing when the 5-DOF rotor fell on the protection bearing. By adjusting the friction coefficient, the initial speed of the rotor, and other parameters, the movement process of the rotor falling in the protection bearing was studied. By comparing the speed fluctuation and contact force of the rotor and the bearing inner race, the influencing factors and rules of the impact of the rotor falling on the protection bearing were analyzed and summarized.

Aiming at the difficulties in quantitatively describing the pollution component information contained in FT-IR spectral data of diesel engine lubricants, wavelet transform and fractal box dimension are used to process FT-IR spectral data and get fractal dimension curve. Multiple peaks can be obtained by analyzing the curve. Two groups of lubricating oil samples with water content of 0.10%, 0.22%, 0.44% and 0.88% respectively and with ethylene glycol content of 0.10%, 0.22%, 0.44% and 0.88% respectively. For the above two groups of oil samples, the corresponding relation between peak position and contaminate ratio can be obtained, and a new method based on FT-IR prediction of contaminate ratio is feasible.

Misalignment is a common failure of rotating machinery, it can cause many problems such as vibration and bearing loss. The research on misalignment usually uses rotor failure test platform, it is a common equipment in various mechanics laboratories that has a mature development. But it still has some defects such as cumbersome operation and high cost. This paper had analyzed the existing rotor failure test platform in the laboratory, according to its actual size and materials, a rigid-flexible hybrid model was established. Based on the method of Adams vibration analysis, the model is simulated and analyzed in parallel misalignment and angle misalignment. According to the results, it was proved that simulation and study of rotor faults based on ADAMS simulation method had a certain feasibility and extensive applicability, and the theoretical basis for virtual prototype was provided.

It is still a great challenge to find an effective degradation indication for health degradation assessment. For that purpose, many computational algorithms have been motivated or suggested. These algorithms commonly use the multiple statistic-based features obtained through calculating the vibration signal to build health indicator which does not consider the internal relevancy among the features. For the purpose of building an effective degradation indicator with pronounced tendency, a novel method is proposed. With the ability of keeping the local structure of data set, Locality preserving projections (LPP) is used to reduce the dimension of the general feature set with the main information remained. Since 2-Dimensional Hidden Markov model (2-D HMM) not only take the relevance between the individuals of fault features into consideration but also capture the global characteristics of the multiple features, 2-D HMM based negative log likelihood probability is built as a new degradation index. The experimental results indicate that the proposed indicator show great abilities to make degradation performance of the bearing and is sensitive to weak defects.

Aiming at monitoring contaminated oil samples of marine engines, the FT-IR spectroscopy is used to analysis the oil samples. Under the condition of laboratory, 18 oil samples with different concentrations and different types of contaminants were measured. The types of contaminants were water, fuel dilution, ethylene glycol and oxidation. The FT-IR spectral data of oil samples is obtained. Firstly, the original FT-IR spectral data are pre-processed by the baseline correction and the data normalization. Then, the dimensions of FT-IR spectral data pre-processed are reduced by the principal component analysis (PCA) and the four different kinds of contaminant oil samples are shown by figures. Lastly, the low dimensional data are clustered by the self-organizing feature map network and the clustered results which stand for different kinds of contaminant oil samples are demonstrated in the digital form. The results showed that the accuracy of oxidation samples clustering reached 100%, the accuracy of contaminant water samples clustering reached 83%, and the accuracy of fuel or ethylene glycol contaminant samples clustering were unsatisfactory.

A method of multi-sensor information fusion is proposed based on Dempster-Shafer evidential theory. To improve the credibility of the evidences, probability statistics and fuzzy membership function are applied to constructing the basic probability assignment function. Then, information from multiple sources is fused by using combination rules of Dempster-Shafer evidential theory. The given method is applied to fault detection of a certain marine diesel engine. Experiment results indicate that multi-sensor information fusion is effective in fault detection by using Dempster-Shafer evidential theory. The presented method has a good performance in accuracy and credibility of the fault detection and the uncertainty of single sensor information is avoided. [PUBLICATION ABSTRACT]