Journal of Vibroengineering

Journal of Vibroengineering, (in Press).Peng Gao Guo, Lei Shi, Yan Fei Yu, Zhan Zhou, Dian Ren MaoWith the decreasing of easy-to-exploit oil and gas resources, complex structure wells put forward higher requirements for wellbore trajectory control accuracy. As the core supporting part of the guiding tool, the radial displacement of the combined bearing directly affects the deflection accuracy of the spindle. In order to reveal the internal correlation mechanism between the radial displacement of the combined bearing and the accuracy of the spindle deflection control, this paper combines the statically indeterminate beam theory and the parametric finite element method to establish a spindle deflection prediction model considering multi-factor coupling. The influence of radial load, rotational speed and inner and outer ring eccentricity on the radial displacement of the combined bearing is systematically studied. The results show that the radial displacement of the combined bearing increases significantly with the increase of radial load, rotational speed and eccentricity. Especially under heavy load conditions, the change of the contact logarithm of the rollers inside the bearing leads to a nonlinear turning of the stiffness characteristics, showing obvious adaptive bearing characteristics. The prototype experiment shows that the variation trend of the simulation and the measured data is highly consistent under the condition of 30 r/min, and the error is controlled within 10 %-15 %, which verifies the accuracy of the model. This study not only quantifies the influence of key parameters on bearing displacement, but also provides a theoretical basis and parameter selection criteria for structural optimization design and accuracy improvement of rotary steering drilling tool combined bearings.

Journal of Vibroengineering, (in Press).Haishan Xu, Hongchao WangShaft vibration (displacement signal) and bearing vibration (velocity signal) are key indicators for evaluating the dynamic characteristics of the rotor and supporting bearing system, and they play a crucial role in the operational performance and safety of equipment. However, in practical applications, collecting shaft vibration or bearing vibration signals often encounters multiple challenges, primarily attributed to limitations in measurement technology, interference from faults, and variations in operating environments. In-depth investigation into the intrinsic correlation between shaft vibration and bearing vibration not only enables data supplementation to improve information completeness, but also offers more precise references for fault diagnosis and condition monitoring. Therefore, this study proposes a method based on homologous information fusion, aiming to explore the intrinsic correlation between shaft vibration and bearing vibration under rub-impact faults. The study first constructs a dynamic model under rub-impact fault condition, and then fuses homologous information using full vector spectrum technology to improve the accuracy of determining the relationship between shaft vibration and bearing vibration at different rotational speeds. Finally, the reliability of simulation results is validated through the establishment of a rotor experimental rig. Experimental results reveal that by mastering this complementary relationship, the operating health status of equipment can be inferred based on the variation tendencies of other critical parameters – even when a specific measured signal is unavailable – and corresponding maintenance and management strategies can thus be formulated.

Journal of Vibroengineering, (in Press).Xiaomin Teng, Huiying Xing, Wansheng Wang, Jing Li, Yunlin MaTo address the critical challenge of low diagnostic accuracy in multistate bearing fault diagnosis caused by inefficient discriminative feature extraction under varying operating conditions, this paper proposes a novel Parameter-weighted Viridis Image Encoding (PVIE) method. Unlike conventional image encoding techniques (e.g., GADF, GASF, MTF, RP) that often suffer from high computational complexity and limited feature separability in complex scenarios, PVIE integrates Variational Mode Decomposition (VMD) with a newly designed Parameter-weighted Euler Difference Feature Extraction (PWEDFE) module. This module explicitly enhances the nonlinearity and periodicity of fault signatures, mapping them into lightweight 2D feature images via Viridis Feature Value Mapping (VFVM). Extensive experiments on two benchmark datasets demonstrate that PVIE achieves exceptional diagnostic accuracies of 99.92 % and 99.74 %, respectively. Compared to state-of-the-art encoding methods, PVIE improves average accuracy by 21.06 % to 39.78 % while reducing diagnostic time by 53.3 %, significantly outperforming existing approaches in both accuracy and efficiency. Furthermore, the method exhibits robust performance under strong noise interference and small-sample scenarios. These results confirm that PVIE offers a substantial advancement over current research by providing a more discriminative, lightweight, and robust solution for real-time industrial fault diagnosis.

Journal of Vibroengineering, (in Press).Hongxi Li, Enhui Zhou, Haishen Jiang, Ling Shen, Zixin YinThe double-layer forced synchronous circular vibrating screen (DLFSCVS) is one of the most effective solutions for material screening. In this paper, a dynamic model was established to control the vibration of the screen, and the vibration characteristics of DLFSCVS are obtained by vibration experiment and parameter analysis. The classification performance of DLFSCVS was studied by EDEM, and the screening mechanism of DLFSCVS was revealed. The results show that the established dynamic model can describe the vibration of DLFSCVS well, and the maximum deviation between the experimental results and the theoretical results was within 4.78 %. The trajectory of the screen box is approximately circular. When the vibration frequency is 14 Hz, the acceleration amplitude of the screen box in the X and Y axis directions is 34.7 and 35.2 m/s2, respectively. With the increase of vibration frequency, the displacement amplitude of the screen box is basically unchanged, and the velocity and acceleration amplitude increase gradually. The results showed that when f= 14 Hz, the screening efficiency of the upper and lower screen plates up to 0.87 and 0.93, respectively.

Journal of Vibroengineering, (in Press).Lianxin Wu, Xiaojie SunAs a core component of mechanical transmission systems, the gearbox's operating state directly determines equipment reliability and industrial production safety. In actual working conditions, a single fault can easily evolve into a complex fault mode with multiple coupled faults. Traditional diagnostic methods face challenges such as insufficient feature extraction and low fault mode discrimination. To address this issue, an intelligent diagnostic model is proposed that integrates adaptive noise complete set empirical mode decomposition (CEEMDAN) feature extraction, multi-scale convolution, and a dual attention mechanism. First, CEEMDAN is used to decompose the vibration signal at multiple scales. After effective IMF filtering, time-domain, frequency-domain, fault-specific, and coupled interactive features are extracted to form a multi-dimensional feature set. Then, adaptive principal component analysis (PCA) is used to reduce the dimensionality to obtain a low-redundancy feature set. Subsequently, a diagnostic model containing multi-scale convolution, a bidirectional long short-term memory network (BiLSTM), and dual attention branches is constructed, and an improved loss function is combined to enhance the ability to distinguish complex fault features. Experimental results based on the Beijing Jiaotong University bogie gearbox bench dataset verify the effectiveness and robustness of the proposed method under complex fault modes, providing a reliable technical solution for gearbox fault diagnosis in industrial scenarios.