Modern engineering systems require advanced uncertainty-aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive-Definite (SPD) matrix constraints. Our methodology features three key innovations: (1) A semi-definite programming-optimized Minimum Volume Ellipsoid model that explicitly quantifies parameter interdependencies while ensuring computational efficiency; (2) A manifold-embedded GPR surrogate model employing Log-Euclidean kernels to intrinsically preserve SPD constraints during uncertainty updating; (3) A Riemannian gradient optimization scheme that enables efficient parameter updates via logarithmic matrix mapping. Validated through mechanical and aerospace case studies, the framework achieves a Log-Euclidean distance of 3.12 x 10-4 in uncertainty updating (compared to a baseline of 48.48) and provides a tenfold computational acceleration over Bayesian alternatives. Robustness tests demonstrate stable performance under 5% noise perturbation, with the Log-Euclidean distance increased only marginally to 1.41 x 10-2. By unifying differential geometry with machine learning, our approach eliminates heuristic projections required in conventional methods while advancing uncertainty quantification through structure-preserving manifold operations. This study bridges geometric consistency, computational efficiency, and physical consistency in uncertainty-aware model updating.

Accurate prediction of the spatial mechanism’s dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation. Traditional engineering models are often inaccurate, primarily because of insufficient experimental data and incomplete understanding of physical phenomena, which impedes model bias reduction in information-poor scenarios. We present a novel hybrid approach aimed at improving the predictive accuracy of the dynamic behavior of spatial deployable mechanisms. The graph convolutional network-temporal convolutional network (GCN-TCN) model, a type of deep learning architecture, is utilized for its expertise in forecasting spatio-temporal data through multi-step predictions. Next, the adaptive bandwidth kernel density estimation technique is applied to estimate the probability density function of residuals from the testing set of the GCN-TCN, quantifying predictive uncertainty. The predictive information is further refined using Bayesian inference, integrating a priori knowledge from physics-based models with data from data-driven models to yield robust posterior predictions. The proposed methodology is validated and shown to be robust through rigorous numerical simulations and experimental validation, demonstrating its ability to provide accurate and reliable predictions for the deployment of spatial mechanisms.

The dynamic stability of steel wire ropes is crucial for crane safety. This article systematically studies the dynamic response of steel wire ropes in crane operations using analytical methods. Modal parameters under prestressing are analyzed, and the influence of load mass on modal frequency is explored. Under different excitation conditions, including sweep load, impact load, and random load, the dynamic response, such as displacement and acceleration, is calculated using modal superposition and Newmark integration methods. The study examines the impact of parameters like prestress, impact load width, and modal damping ratio on the dynamic response. Results show that increased load mass significantly raises the transverse modal frequency, while impact pulse width and modal damping ratio strongly influence vibration response. This research provides a theoretical basis and technical support for the design, use, and maintenance of crane wire ropes.

Uncertainty model updating techniques are pivotal for improving the accuracy of numerical models in the presence of uncertainties. However, current interval-based methods often fail to account for parameter correlations and suffer from computational inefficiency, particularly with complex numerical models. This study introduces a novel interval uncertainty model updating framework based on ellipsoidal convex model similarity. Initially, the ellipsoidal convex model is utilized to represent the bounded uncertainties of parameters along with their correlations, thereby addressing the shortcomings of traditional interval models that presume parameter independence. Subsequently, a novel metric combining Euclidean distance and Riemannian distance is employed to assess both the central distance and the shape similarity within the parameter space. Moreover, an efficient parameter updating strategy is developed by incorporating a dual-layer surrogate model—comprising kriging and transformer—with Riemannian gradient descent. This approach significantly reduces computational overhead while preserving the geometric constraints inherent to the ellipsoidal space. The proposed framework’s efficacy is confirmed through both numerical simulations and experimental case studies, showcasing its precision in updating parameter bounds and correlations. This research offers a robust and efficient methodology for interval model updating, suitable for practical engineering applications involving correlated uncertainties.

Bearing skidding is a frequent phenomenon in rotating machinery, which causes equipment motion instability and bearing wear failure. Due to the difficulties in the manufacture of high-speed flexible rotor test rig, measurement of bearing motion parameters and complex structure of angular contact ball bearing (ACBB), which makes the research on double-piece inner ring ACBB skidding challenging. This paper studies the ACBB skidding mechanism from the aspect of rotor characteristics, and first finds the resonance skidding phenomenon. Firstly, a high-speed flexible rotor test rig is designed and built to study the ACBB skidding mechanism, and the influence of foreign matter on the bearing cage slip rate is systematically studied. Then, the bearing cage speed considering the influence of lubricating oil pollution is accurately measured based on the adaptive fractional short-time Fourier transform. Finally, bearing skidding diagnosis under variable working conditions is realized based on the deep meta-transfer learning with feature enhanced generative adversarial network and average deflection power threshold. These proposed strategies systematically solve some problems in the research field of ACBB skidding, which has high practical significance and theoretical guidance value.

Engineering tests can yield inaccurate data due to instrument errors, human factors, and environmental interference, introducing uncertainty in numerical model updating. This study employs the probability-box (p-box) method for representing observational uncertainty and develops a two-step approximate Bayesian computation (ABC) framework using time-series data. Within the ABC framework, Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps, respectively. A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty, resulting in rapid convergence and accurate parameter estimation with minimal iterations. The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis. The results affirm the efficiency, robustness, and practical applicability of the proposed method.

Aiming at the complexity of the mill transmission system structure,the uncertainty of the con-straint conditions among the components and the nonlinearity,a finite element model correction method based on the PSO-BP neural network is proposed in this study.This method approximates the nonlinear mapping rela-tionship between the two by improving the back propagation(BP)neural network,combines with the actual struc-tural response,and uses the generalization property of the neural network to obtain the numerical value of the model design parameters.After the correction,the frequency error is reduced from a maximum of 18%to about 4%,and the error range of the correction coefficient is all within 0.5%,while obviously improving the accuracy of the finite element model.Meanwhile,it does not need a large number of iterative solving steps,avoids the com-plex nonlinear optimization process of the traditional inverse problem model modification method,improves the efficiency,verifies the feasibility of the PSO-BP neural network method applied to the transmission system of large mill,and lays a foundation for the overall analysis of the subsequent transmission system.

To obtain the posterior distribution of parameters for expensive numerical models with complex dynamic responses that require substantial computational resources, a model updating method utilizing the Wasserstein distance as uncertainty quantification (UQ) metric and variational Bayesian Monte Carlo (VBMC) for parameter posterior identification is proposed. Combined with MATLAB, Nastran and Adams software for secondary development, the traditional finite element model updating technology is extended to the rigid-flexible coupling model. The application results in the composite plate, satellite rigid-flexible model, and solar wing deployment model demonstrate that the updated models exhibit high precision. Compared to existing UQ methods, the Wasserstein distance can more robustly measure the discrepancy between two samples and significantly reduce model structure and parameter uncertainty. The VBMC method converges to the true parameter values after a few number of iterations, and the updating efficiency is markedly higher than that of the random-sampling-based Markov chain Monte Carlo (MCMC) method.

Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.

针对电路板焊点的疲劳失效问题,首先使用基于灵敏度的模型修正方法对有限元模型参数进行了修正,并通过模态试验和振动试验验证了模型的精度.然后通过随机振动试验获得应力-时间历程的载荷谱,再通过瞬态响应分析获得危险焊点的应力响应时域信号.最后运用雨流计数法和Miner累积损伤理论对焊点进行疲劳寿命分析,对比了模型修正前后误差对疲劳寿命的影响,为印制电路板的疲劳寿命分析提供了参考.

为了确保磨机传动系统具有足够的可靠性,避免系统停机维修而造成损失,在设计之初就对传动系统进行较全面的可靠性计算十分必要.磨机传动系统承受由转矩、扭转振动和磨机横向振动带来的载荷.通过扭振分析识别运行时的频率,结合主要激励频率绘制坎贝尔图,计算得到磨机正常工作时的转矩放大因子.根据工程经验,在工程应用中磨机故障的多发位置往往是小齿轮轴.因此,对正常工作时产生的转矩、横向载荷进行处理,分别计算不同载荷下小齿轮轴的可靠性,可以有效保证磨机的正常可靠运行,降低因小齿轮轴故障造成的损失.

为了改善地铁供风系统的动力学性能,提高地铁车辆运行平稳性、舒适性,采用计权加速度法(SWAT)识别了空压机组件工作状态下的动态载荷,并加载于供风系统有限元模型中,计算系统动应力分布和加速度响应.以供风系统有失效风险位置的动应力、空压机质心加速度RMS最小为优化目标,以3个吊挂减振器刚度、阻尼为设计参数,采用遗传算法进行参数优化,并进行了试验验证.优化后关键点平均动应力、加速度的RMS值分别下降了23.2％、5.4％.研究结果表明,本方法能够明显地改善供风系统结构动响应,减小地铁供风系统的结构振动,提高系统动态性能.将大大改善由地铁供风系统空压机振动引起的地铁振动舒适性.

为了获得机械结构的精确模型,采用以逆响应面的有限元模型修正理论为基础的方法.逆响应面法直接拟合出设计参数(密度、弹性模量等)关于特征参数(固有频率等)的显式表达式,以特征参数目标值带入逆响应面函数得到修正后的设计参数值,相较于响应面法,无需迭代寻优,减少计算工作量.以齿轮轴和磨机传动轴为例,实现了以逆响应面法为基础的有限元模型修正,并计算修正前后模态频率误差,结果显示:逆响应面法模型修正效果较优于响应面法,进而验证了逆响应面法在机械结构动力学特性分析中的有效性.

The development of advanced inclination sensors with high resolution has long been a demand for modern industry areas. Herein, in this work, we propose and demonstrate a novel ultra-high-resolution uniaxial inclination sensor rooting in diamagnetic levitation technique without control system. The sensor is mainly composed of a stable levitated diamagnetic mass (graphite rod) and a pair of rationally designed small permanent magnets, permitting a simple structure without additional power supply need. Inclination monitoring is realized by measuring the unidirectional displacement of the graphite rod using optical approach. The experimental results reveal that the measurement range of the sensor can reach +/- 0.640 degrees with an ultra-high resolution better than 0.69 '', a linear measurement range of +/- 0.621 degrees with a nonlinearity of - 4.263% and a sensitivity of 5.590 mm/degrees. Simulations were performed and the numerical results were in good agreement with the experimental measurement. Accompanied by the simplistic structural feature, such high resolution and excellent linearity suggest the feasibility and applicability of this novel sensor for numerous precision inclination measurement applications.

采用模型修正技术修正轴承-转子系统部件的有限元模型;利用ADAMS多体动力学仿真软件建立圆柱滚子轴承-转子系统的刚柔耦合模型;基于此刚柔耦合模型对圆柱滚子轴承进行打滑过程运动仿真,研究转速、径向载荷、游隙以及摩擦因素对轴承打滑率的影响;设计轴承-转子系统试验台,对圆柱滚子轴承进行打滑率测试试验,研究载荷、转速以及游隙对轴承打滑的影响,验证仿真模型的准确性;在此基础上研究轴承运转过程中,多种参数可能造成打滑的边界条件.

针对风电齿轮箱行星架阶梯轴处的应力集中现象,采用过渡多圆弧的改进措施.基于多圆弧曲线的通用几何关系,联合MATLAB和HyperWorks对多圆弧结构进行参数化建模.以多圆弧结构质量最小为优化目标,应力符合强度要求为约束条件建立优化问题,结合代理模型和粒子群算法对多圆弧结构进行优化设计,优化后的应力降低了9.8％,满足疲劳强度要求,优化了时间成本.

以轨道车辆地板组合断面为研究对象,为了获得精确的振动响应计算模型,采用基于模态特征的有限元修正方法,对结构较为复杂的复合材料客室地板进行了有限元模型修正,使用修正后的模型进行模态参数、频响函数的试验和仿真验证,结果表明,在50 Hz以内试验与仿真结果匹配性较好.基于修正后的地板组合断面有限元模型,研究了不同座椅安装方式对地板断面振动响应及传递规律的影响,研究结果与现车的振动规律一致,从理论上验证了现车座椅安装改善方案的合理性.

叶片式阻尼器出力特性具有较强的非线性,为建立准确的力学模型,以实验室的一种叶片式阻尼器为研究对象,对其进行示功试验以获得它的出力特性;以双曲正切模型和双曲正切改进模型为基础,依据示功试验的实验数据选用顺序选择遗传算法辨识出这两种模型的参数,并在Matlab中进行出力仿真.结果表明:仿真得到的示功曲线与实验的示功曲线较为吻合,验证了这两种模型的准确性,双曲正切改进模型对比原模型具有更高的精度.

列车用橡胶减振器的性能关系到列车运行的平稳性和乘员的舒适性.橡胶作为一种典型的超弹性材料,在工程应用中存在较大的不确定性.基于模型确认方法,以橡胶结构件为研究对象,首先对橡胶试样进行了单轴拉伸实验,获得材料的应力-应变曲线,拟合曲线得到Mooney-Rivlin模型的参数,并估计和识别参数的不确定性;然后对试样进行了简单剪切实验,获得频域黏弹性材料模型参数及其不确定性;最后建立了橡胶减振器超弹性-黏弹性模型,通过计算获得了小振幅下橡胶减振系统的动态特性,并对减振结构固定频率下的加速度响应值进行量化分析,验证了橡胶减振器超弹性-黏弹性模型的合理性.

贝叶斯模型修正框架下,以频响函数作为目标,提出了一种使用近似似然函数的不确定性模型修正方法.相比于模态参数,频响函数包含了结构更加充分的信息,用于结构动力学模型修正时有诸多优点,但现有的不确定性模型修正方法并不能很好地实现将频响函数作为目标进行修正.针对此问题,介绍了频响函数和贝叶斯框架下的不确定性模型修正理论,基于近似贝叶斯计算提出了一种近似似然函数,可适用于频响函数作为目标进行不确定性修正.将提出的似然函数应用到三自由度数值和H型非对称梁的有限元模型修正算例中,并结合DREAM算法对不确定性参数进行识别.研究结果表明:修正后参数的上、下限与目标值相差无几,修正后模型的频响函数与目标值几乎重合,在一定噪声水平下仍具有较好的修正效果,验证了所提方法的有效性.