Mechanical Systems and Signal Processing最新文献_第5页

Dynamic response and sliding mode control of a cold rolling mill subjected to harmonic and Gaussian colored noise excitations 冷轧机在谐波和高斯有色噪声激励下的动态响应与滑模控制

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ymssp.2026.113971

Xiaofei Chen , Wei Zhang , Yufei Zhang

During operation, cold rolling mills are susceptible to the coupled effects of random excitations and structural nonlinearities, which can induce complex dynamic behaviors that adversely affect rolling quality and equipment safety. This paper studies the structural dynamic characteristics and vibration suppression for a two-degree-of-freedom cold rolling mill vertical structure model under combined harmonic and random excitation for the first time. Firstly, an averaging method and a stochastic method are extended to derive the amplitude-frequency and steady-state response equations, respectively. Secondly, the response shows the mill exhibits nonlinear hard spring characteristics and bistability in the resonance region. The coexistence and evolution of low- and high-amplitude attractors are further elucidated via the equivalent potential energy diagram and basin of attraction. Additionally, random excitation is a key factor inducing chaotic behavior in the rolling mill. Finally, Gaussian colored noise induces stochastic switching, stochastic P- and D-bifurcations. This can lead to defects in the rolled products, and in severe cases, it may even threaten the safe operation of the rolling mill. To suppress this catastrophic switching, this paper innovatively introduces the improved double power exponential reaching law to design sliding mode control, achieving faster convergence, suppressing chattering and reducing energy consumption. The proposed control has been rigorously proven to be stable and has been effectively verified through numerical simulations. The research findings provide essential theoretical foundations and technical support for the safe design and manufacture of vertical structural models for cold rolling mills in engineering practice.

冷轧机在运行过程中容易受到随机激励和结构非线性的耦合影响，产生复杂的动力行为，对轧制质量和设备安全产生不利影响。本文首次研究了二自由度冷轧机垂直结构模型在谐波和随机联合激励下的结构动力特性和振动抑制问题。首先，推广了平均法和随机法，分别推导了幅频响应方程和稳态响应方程。其次，在共振区，磨机具有非线性硬弹簧特性和双稳性。通过等效势能图和引力盆地进一步阐明了高低幅吸引子的共存和演化。此外，随机激励是引起轧机混沌行为的关键因素。最后，高斯色噪声诱导随机切换、随机P分岔和d分岔。这会导致轧制产品出现缺陷，严重时甚至会威胁到轧机的安全运行。为了抑制这种突变开关，本文创新性地引入改进的双功率指数趋近律来设计滑模控制，实现了更快的收敛、抑制抖振和降低能耗。通过数值仿真验证了所提出的控制方法是稳定的。研究结果为工程实践中冷轧机立式结构模型的安全设计与制造提供了必要的理论依据和技术支持。

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引用次数: 0

Aligned sparse non-negative matrix factorization for vehicle-track features decoupling 车辆-轨道特征解耦的对齐稀疏非负矩阵分解

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ymssp.2026.113907

Jiyuan Huo , Jianwei Yang , Dechen Yao , Zhongshuo Hu , Yuanting Dai , Bin Zhu

Vibration signals collected from in-service urban rail vehicles exhibit strong coupling between vehicle dynamics and track geometry excitations, often compounded by environmental noise. This poses a significant challenge for the accurate decoupling of sources and the estimation of track geometric parameters, particularly curve superelevation, from vehicle acceleration data. To address this, we propose an Aligned Sparse Non-negative Matrix Factorization (ASNMF) framework to decouple of vehicle-track features: A Kurtosis-Spectral Peak (KSP) criterion is first applied to construct a Hankel matrix that enhances the representation of non-stationary features; A multi-objective optimization is then formulated by integrating a Gini-based sparsity constraint and a Maximum Mean Discrepancy (MMD) alignment term to ensure consistent component extraction; The resulting multiplicative updating algorithm yields physically interpretable decompositions. Validation using both simulated and real-world vibration data demonstrates that ASNMF effectively separates vehicle and track-induced responses under strong coupling and noise. Compared with existing matrix factorization and blind source separation methods, ASNMF achieves higher signal fidelity and more accurate track-related feature estimation, offering a robust and novel solution for decoupling and interpreting coupled vehicle–track dynamic responses under non-stationary operating conditions.

从在役城市轨道车辆收集的振动信号显示车辆动力学和轨道几何激励之间的强耦合，通常与环境噪声混合。这对从车辆加速度数据中准确解耦和估计轨道几何参数（特别是曲线超高程）提出了重大挑战。为了解决这个问题，我们提出了一个对齐稀疏非负矩阵分解（ASNMF）框架来解耦车辆-轨道特征：首先应用峭度-谱峰（KSP）准则来构建一个增强非平稳特征表示的Hankel矩阵；然后，通过整合基于gini的稀疏性约束和最大平均差异（MMD）对齐项来制定多目标优化，以确保提取的成分一致；由此产生的乘法更新算法产生物理上可解释的分解。仿真和实际振动数据验证表明，ASNMF在强耦合和强噪声条件下有效地分离了车辆和轨道引起的响应。与现有的矩阵分解和盲源分离方法相比，ASNMF实现了更高的信号保真度和更精确的轨道相关特征估计，为非平稳工况下耦合车辆-轨道动态响应的解耦和解释提供了一种鲁棒的新解决方案。

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引用次数: 0

Dynamic predictive maintenance framework for mechanical systems via uncertainty-aware RUL estimation 基于不确定性感知规则估计的机械系统动态预测性维护框架

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.ymssp.2026.113977

Lubing Wang, Ying Chen, Zhengbo Zhu, Xufeng Zhao

In prognostics and health management for mechanical systems, the uncertainty of remaining useful life (RUL) assessment caused by noise interference and measurement errors is often overlooked, which may lead to inaccurate maintenance results. To solve these challenges, this study presents a predictive maintenance framework that integrates uncertainty-aware RUL estimation to support maintenance decisions and spare parts management. We first introduce a hybrid model that combines bidirectional gated recurrent units with an integrated global and local multi-head sparse attention mechanism to capture long-term dependencies and transient patterns, while employing Monte Carlo dropout for quantifying RUL uncertainty. Using RUL uncertainty estimation, three distinct predictive maintenance models and spare parts ordering models are formulated. These models integrate estimated mean RUL, lower bounds, and maintenance costs to dynamically determine the optimal maintenance time and spare parts ordering time during periodic inspections. Validated on aero-engine and industrial machine datasets, the method outperforms existing strategies, achieving effective fault prevention and reducing the maintenance cost rate by over 50%. This work provides a practical solution for reliable and cost-effective mechanical systems by linking uncertainty-aware RUL estimation with maintenance decisions.

在机械系统的预测和健康管理中，噪声干扰和测量误差引起的剩余使用寿命（RUL）评估的不确定性往往被忽视，从而可能导致不准确的维护结果。为了解决这些挑战，本研究提出了一个预测性维护框架，该框架集成了不确定性感知规则估计，以支持维护决策和备件管理。我们首先引入了一个混合模型，该模型将双向门控循环单元与集成的全局和局部多头稀疏注意机制相结合，以捕获长期依赖关系和瞬态模式，同时使用蒙特卡罗dropout来量化RUL的不确定性。利用规则不确定性估计，建立了三种不同的预测维修模型和备件订购模型。这些模型集成了估计的平均RUL、下限和维护成本，以动态确定定期检查期间的最佳维护时间和备件订购时间。在航空发动机和工业机器数据集上进行了验证，该方法优于现有策略，实现了有效的故障预防，并将维护成本率降低了50%以上。这项工作通过将不确定性感知RUL估计与维护决策联系起来，为可靠和经济的机械系统提供了一个实用的解决方案。

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引用次数: 0

Fractional-order stochastic resonance-based rescaling-frequency scanning images for early multi-frequency fault detection of machines 基于分数阶随机共振的重标频扫描图像用于机器早期多频故障检测

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ymssp.2026.113944

Yanan Gai , Zijian Qiao , Yanglong Lu , Ronghua Zhu , Xin Zhang

In engineering applications, weak multi-frequency fault signals from mechanical equipment are often masked by strong background noise. Traditional stochastic resonance (SR) methods mainly focus on enhancing fault signals into sine-like ones, but they may lose or even destroy the multi-harmonic characteristics of fault signals. To this end, this paper would propose a rescaling-frequency scanning image method using fractional-order SR (FSR-RFSI), aiming to enhance and visualize weak multi-frequency useful signals. First, the proposed method develops a fractional-order SR system with memory properties, which is designed to detect weak multi-frequency signals in complex spectral environments. Moreover, a weighted zero-crossing signal-to-noise ratio (WZCSNR) is proposed as a performance evaluation metric, which effectively overcomes the limitation of the traditional signal-to-noise ratio (SNR) that focuses solely on frequency-domain energy while neglecting time-domain multi-harmonic components. Meanwhile, to improve parameter tuning efficiency, this paper establishes an analytical relationship map between the resonant frequency and system parameters, namely rescaling-frequency scanning image. Furthermore, a quantum genetic algorithm (QGA) is used to achieve adaptive optimization of key system parameters. Simulation analyses and experiments on early rolling bearing and gearbox faults show that the proposed method can effectively boost and detect weak multi-frequency fault signals. Additionally, comparative analysis with Maximum Correlated Kurtosis Deconvolution (MCKD), Fast Kurtogram (FK), and Feature Modal Decomposition (FMD) methods further validates the superiority of the proposed method.

在工程应用中，机械设备微弱的多频故障信号往往被强背景噪声所掩盖。传统的随机共振方法主要是将故障信号增强为类正弦信号，但可能会失去甚至破坏故障信号的多谐特性。为此，本文提出了一种基于分数阶SR （FSR-RFSI）的重标频扫描图像方法，旨在增强和显示微弱的多频有用信号。首先，该方法开发了一种具有记忆特性的分数阶SR系统，用于检测复杂频谱环境下的微弱多频信号。此外，提出了加权过零信噪比（WZCSNR）作为性能评价指标，有效克服了传统信噪比（SNR）仅关注频域能量而忽略时域多谐波分量的局限性。同时，为了提高参数调谐效率，本文建立了谐振频率与系统参数的解析关系图，即重标频扫描图像。此外，采用量子遗传算法（QGA）实现系统关键参数的自适应优化。对滚动轴承和齿轮箱早期故障的仿真分析和实验表明，该方法可以有效地增强和检测微弱的多频故障信号。此外，通过与最大相关峰度反卷积（MCKD）、快速峰度图（FK）和特征模态分解（FMD）方法的对比分析，进一步验证了该方法的优越性。

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引用次数: 0

Cutting force modeling and machinability analysis for ultrasonic vibration-assisted thread milling of SiCp/Al composites 超声振动辅助螺纹铣削SiCp/Al复合材料切削力建模及可加工性分析

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ymssp.2026.113973

Ziyang Zhang , Daohui Xiang , Chaosheng Song , Shuaikun Yang , Yanqin Li , Peicheng Peng , Bo Li , Guofu Gao , Yanyan Yan , Jinglin Tong

In this study, ultrasonic vibration thread milling (UVTM) was employed to machine small-diameter threaded holes (below M4). A predictive force model for UVTM incorporating the instantaneous undeformed chip thickness was established. The machining process and its outcomes were evaluated through analysis of milling force signals and thread morphology. First, a dynamic tool model was developed based on the kinematic characteristics of UVTM. Subsequently, a geometric model of the instantaneous undeformed chip thickness was constructed based on thread cutter parameters. A comprehensive milling force prediction model was then formulated, integrating chip formation, extrusion friction, ultrasonic impact, particle crushing, and particle debonding forces. Experimental validation confirmed the model’s accuracy, with average prediction errors of 13.44 % and 10.83 % for the two machining passes, and corresponding standard deviations of 12.01 % and 11.58 %, respectively. Compared with conventional thread milling, UVTM significantly improved thread surface morphology and reduced machining-induced defects. Force signal analysis further revealed that ultrasonic vibration effectively reduces milling forces, mitigates tool wear, and suppresses cutting vibration.

本研究采用超声振动螺纹铣削（UVTM）加工小直径螺纹孔（M4以下）。建立了考虑瞬时未变形切屑厚度的UVTM预测力模型。通过铣削力信号和螺纹形貌分析，评价加工过程及其效果。首先，根据UVTM的运动特性，建立了刀具动态模型；在此基础上，建立了基于螺纹刀参数的切屑瞬时不变形厚度几何模型。结合切屑形成、挤压摩擦力、超声冲击、颗粒破碎力和颗粒脱粘力，建立了综合铣削力预测模型。实验验证了该模型的准确性，两道工序的平均预测误差分别为13.44%和10.83%，相应的标准差分别为12.01%和11.58%。与传统螺纹铣削相比，UVTM显著改善了螺纹表面形貌，减少了加工缺陷。力信号分析进一步表明，超声振动能有效降低铣削力，减轻刀具磨损，抑制切削振动。

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引用次数: 0

Composite control technology for rapid position alignment of inertial reference unit under high-frequency resonant constraints 高频谐振约束下惯性参考单元快速定位的复合控制技术

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ymssp.2026.113974

Tuo Weixiao, Wang Tianyu, Li Xingfei

The Inertial Reference Unit (IRU) isolates base angular vibrations in optical tracking and pointing systems. It faces multi-source heterogeneous disturbances, particularly high-frequency resonances that constrain the use of high-gain controllers. To achieve fast and accurate position alignment, this paper proposes a composite control structure for IRU position control, designed according to disturbance frequency characteristics. The structure integrates a noise-attenuating PID (NA-PID) controller, a two-stage band-pass disturbance observer (2sDOB), and a band-pass feedforward branch, with performance validated through simulations and experiments. Results show that NA-PID extends the position control bandwidth to 161 Hz without exciting high-frequency resonances. The 2sDOB eliminates step-response oscillations induced by low-frequency resonance, achieving a settling time of 18.0 ms and a pointing accuracy of 0.876 μrad RMS. The modified feedforward branch increases the base angular vibration suppression bandwidth to 51.1 Hz.

惯性参考单元（IRU）在光学跟踪和指向系统中隔离基本角振动。它面临多源异质干扰，特别是高频共振，限制了高增益控制器的使用。为了实现快速准确的位置对准，本文提出了一种根据干扰频率特性设计的IRU位置控制复合控制结构。该结构集成了噪声衰减PID （NA-PID）控制器、两级带通干扰观测器（2sDOB）和带通前馈支路，并通过仿真和实验验证了其性能。结果表明，在不激发高频共振的情况下，NA-PID将位置控制带宽扩展到161 Hz。2sDOB消除了低频共振引起的阶跃振荡，稳定时间为18.0 ms，指向精度为0.876 μrad RMS。改进后的前馈支路将基底角振动抑制带宽提高到51.1 Hz。

引用次数: 0

A novel multi-scale dense residual shrinkage GAN for data-limited rotating machinery fault diagnosis 基于多尺度密集残余收缩GAN的旋转机械故障诊断

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ymssp.2026.113906

Tongqiang Yi , Yongjie Shi , Xiangnan Jing , Jiang Guo , Fang Yuan , Wenyang Lei

Fault diagnosis of rotating machinery is crucial for industrial safety, yet practical applications face significant challenges, including high data acquisition costs, scarce fault samples, and severe class imbalance, which severely limit the performance of deep learning diagnostic models. This paper proposes a multi-scale dense residual shrinkage generative adversarial network (MDRS-GAN) specifically designed to address fault diagnosis under limited data conditions. The method innovatively introduces a multi-scale generator that employs multiple sub-generators working collaboratively with a multi-head attention mechanism to achieve dynamic fusion of multi-scale features, significantly enhancing sample distribution simulation capabilities. Simultaneously, a hybrid time–frequency discriminator based on dense-block deep residual shrinkage networks is constructed, integrating dense connections, an improved efficient channel attention mechanism, and soft-thresholding denoising techniques to enhance sensitivity to critical features in both time and frequency domains, achieving dual functionality of sample authenticity recognition and fault classification. Additionally, a Bayesian optimization strategy is introduced to adaptively adjust discriminator hyperparameters, improving model training stability and efficiency. Extensive experiments on CWRU and XJTU-SY bearing datasets demonstrate that: generated samples achieve feature distribution similarity exceeding 0.8 (maximum 0.91) with real samples; under extreme small-sample conditions (only 2 samples per class), fault diagnosis accuracies reach 94.32% and 95.83% respectively; under severe class imbalance (100:1), accuracies maintain at 96.46% and 96.06%. Compared with existing methods, MDRS-GAN shows significant advantages across all evaluation metrics, providing an effective solution for data-limited rotating machinery fault diagnosis in industrial scenarios.

旋转机械的故障诊断对工业安全至关重要，但实际应用面临着巨大的挑战，包括数据采集成本高、故障样本稀缺以及严重的类别不平衡，这些都严重限制了深度学习诊断模型的性能。针对有限数据条件下的故障诊断问题，提出了一种多尺度密集剩余收缩生成对抗网络（MDRS-GAN）。该方法创新性地引入了一种多尺度发生器，利用多个子发生器协同工作，采用多头注意机制实现多尺度特征的动态融合，显著增强了样本分布仿真能力。同时，构建了基于密集块深度残差收缩网络的混合时频鉴别器，将密集连接、改进的高效通道关注机制和软阈值去噪技术相结合，增强了对时域和频域关键特征的敏感性，实现了样本真实性识别和故障分类的双重功能。此外，引入贝叶斯优化策略自适应调整鉴别器超参数，提高了模型训练的稳定性和效率。在CWRU和XJTU-SY轴承数据集上的大量实验表明：生成的样本与真实样本的特征分布相似度超过0.8（最大0.91）；在极端小样本条件下（每类只有2个样本），故障诊断准确率分别达到94.32%和95.83%；在严重的类不平衡（100:1）下，准确率维持在96.46%和96.06%。与现有方法相比，MDRS-GAN在所有评估指标上都显示出显著的优势，为工业场景下数据有限的旋转机械故障诊断提供了有效的解决方案。

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引用次数: 0

Vehicle-based bridge health monitoring with limited data: a physics-guided TimeGAN and multi-view feature fusion framework 基于有限数据的车辆桥梁健康监测：物理引导的TimeGAN和多视图特征融合框架

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.ymssp.2026.113949

Yifu Lan , Robert Corbally , Flavio Bono , Daniel Tirelli , Abdollah Malekjafarian

In recent years, indirect bridge health monitoring (iBHM) using vehicle-mounted sensors has gained increasing attention due to its low-carbon footprint and cost-efficiency. The rapid development of artificial intelligence (AI) has further promoted its potential for industrial deployment and scalability. However, the limited availability and high noise levels of drive-by measurements often hinder its practical implementation. To address these, this study proposes a hybrid framework that integrates physics-informed data augmentation with multi-view data fusion and unsupervised learning strategies. PyTiGAN, a physics-guided time-series GAN, is developed to synthesize high-fidelity drive-by data using a physics vehicle-bridge interaction (VBI) kernel. These generated data are then combined with real measurements for structural state identification. A multi-view dimensionality reduction and fusion scheme is designed to extract discriminative features from various sensors and embed them into a compact fused space. The framework was validated using field-test data from a testbed bridge structure and a vehicle at the testing site in Ispra, Italy, as part of the MITICA (MonItoring Transport Infrastructures with Connected and Automated Vehicles) project. The results confirm that the framework can accurately detect both minor and moderate bridge damage using only limited drive-by data. Sensitivity analyses further examine how synthetic data volume, physics-based kernels, embedding dimensions, and sensor placement influence damage detection performance. The proposed method demonstrates a promising solution for iBHM under data scarcity.

近年来，基于车载传感器的桥梁间接健康监测（iBHM）因其低碳足迹和成本效益而受到越来越多的关注。人工智能（AI）的快速发展进一步提升了其产业部署和可扩展性的潜力。然而，驾驶测量的有限可用性和高噪声水平经常阻碍其实际实施。为了解决这些问题，本研究提出了一个混合框架，该框架将物理信息数据增强与多视图数据融合和无监督学习策略相结合。PyTiGAN是一种物理引导的时间序列GAN，用于使用物理车桥交互（VBI）内核合成高保真的行车数据。然后将这些生成的数据与实际测量数据相结合，用于结构状态识别。设计了一种多视图降维融合方案，从各种传感器中提取判别特征，并将其嵌入到紧凑的融合空间中。作为MITICA（连接和自动车辆监控交通基础设施）项目的一部分，该框架使用来自意大利Ispra试验场的试验台桥梁结构和车辆的现场测试数据进行了验证。结果表明，该框架仅使用有限的驱动数据就能准确地检测出桥梁的轻微和中度损伤。灵敏度分析进一步研究了合成数据量、基于物理的核、嵌入维度和传感器位置如何影响损伤检测性能。该方法为数据稀缺条件下的iBHM提供了一个很好的解决方案。

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引用次数: 0

Dynamic characteristics analysis of a spalled bearing-rotor system for high-speed trains under combined excitations 联合激励下高速列车剥落轴承-转子系统动态特性分析

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ymssp.2026.113943

Yuewei Yu , Guokai Jiao , Bo Li , Leilei Zhao , Chuanbo Ma

With the continuous increase in the operating speed of high-speed trains, faults for rotor-bearing systems have become increasingly prominent, posing a serious threat to train’s operation safety. Among the various faults of rotor-bearing systems, bearing spalling not only affects the normal operation of the bearing itself but also alters the dynamic characteristics of the rotor-bearing system, thereby inducing additional frequency components and super-harmonic resonances, which greatly increase the difficulty of fault identification in train rotor-bearing systems. This paper focuses on spalling in deep-groove ball bearings of traction-motor rotor-bearing systems. Based on an analysis of the coupling among multiple excitations, a dynamic model is developed that simultaneously accounts for rotor unbalanced magnetic pull, mechanical unbalance forces, rubbing forces, gravity, the nonlinear supporting forces of a cylindrical roller bearing and the nonlinear supporting forces of deep-groove ball bearing with spalling. The model is validated against test-rig experiments. Quantitative analysis in the speed range of 1 000–5 000 rpm shows that outer-race, inner-race and rolling-element spalling generate clearly distinguishable fault characteristic bands and modulation sidebands. When rolling elements pass over a local spall, the contact force exhibits instantaneous jumps and intermittent loss, so that the RMS value of the nonlinear supporting force becomes about 2.4–3.4 times that of the corresponding healthy bearing. As the spall width increases, the RMS acceleration and supporting force grow monotonically at all speeds, with the amplification especially pronounced at 5 000 rpm. These quantitative findings provide a modeling reference and theoretical basis for fault diagnosis of rotor-bearing systems in high-speed trains.

随着高速列车运行速度的不断提高，转子轴承系统故障日益突出，对列车运行安全构成严重威胁。在转子-轴承系统的各种故障中，轴承剥落不仅影响轴承本身的正常运行，还会改变转子-轴承系统的动态特性，从而诱发附加频率分量和超谐波共振，大大增加了列车转子-轴承系统故障识别的难度。本文主要研究牵引电机转子-轴承系统中深沟球轴承的剥落问题。在分析多种激励耦合的基础上，建立了同时考虑转子不平衡磁拉力、机械不平衡力、摩擦力、重力、圆柱滚子轴承的非线性支撑力和带剥落深沟球轴承的非线性支撑力的动力学模型。通过试验验证了模型的有效性。在1 000 ~ 5 000 rpm转速范围内的定量分析表明，外圈、内圈和滚动元件剥落产生了明显可区分的故障特征带和调制边带。当滚动体通过局部小块时，接触力表现出瞬时跳跃和间歇性损失，使得非线性支撑力的均方根值约为相应健康轴承的2.4-3.4倍。随着小片宽度的增加，在所有速度下，RMS加速度和支撑力单调增长，在5000rpm时放大尤为明显。这些定量研究结果为高速列车转子轴承系统的故障诊断提供了建模参考和理论依据。

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引用次数: 0

A Real-Time inverse method for moving contact force identification considering structural characteristics of Pantograph–Catenary system 考虑受电弓接触网结构特性的运动接触力实时反演识别方法

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ymssp.2026.113972

Haifei Wei, Ning Zhou, Xingshuai Zhi, Yao Cheng, Hongming Chen, Weihua Zhang

High-accuracy identification of contact force has long been a critical topic in the state monitoring of pantograph–catenary systems (PCS). This force contains key information for assessing current collection quality and diagnosing faults in both the pantograph and the catenary. With increasing train speeds and the emergence of more complex service conditions, traditional contact force measurements—typically limited to frequencies below 20 Hz—are no longer adequate. To address this limitation, this paper proposes a novel method for real-time contact force identification based on an inverse problem framework. First, a generalized elastically supported beam model is developed to describe the pantograph contact strip, allowing for accurate reconstruction of boundary conditions and load–response relationships. Second, a sliding window strategy is integrated with a sparse regularization technique, incorporating load dictionary matching and static force constraints, to enable online inversion of moving contact forces with high robustness and low latency. Based on the data of PCS simulation, the proposed method was validated to be effective and robust in identifying the contact force with complex characteristics. Furthermore, lab tests verified its effectiveness and feasibility for engineering applications. In addition, the discussion results indicate that the proposed approach exhibits low dependence on measurement point locations, strong capability in identifying impact loads, and good real-time performance. The approach offers a new and effective solution for wide frequency domain contact force identification in high-speed and variable operating environments.

接触力的高精度识别一直是受电弓接触网系统状态监测中的一个关键问题。该力包含评估电流收集质量和诊断受电弓和接触网故障的关键信息。随着列车速度的提高和更复杂的服务条件的出现，传统的接触力测量-通常限于低于20hz的频率-不再适用。为了解决这一问题，本文提出了一种基于逆问题框架的实时接触力识别方法。首先，建立了一个广义的弹性支承梁模型来描述受电弓接触带，从而可以精确地重建边界条件和载荷-响应关系。其次，将滑动窗口策略与稀疏正则化技术相结合，结合负载字典匹配和静力约束，实现了高鲁棒性和低延迟的运动接触力在线反演。基于PCS仿真数据，验证了该方法在识别具有复杂特性的接触力方面的有效性和鲁棒性。实验验证了该方法的有效性和工程应用的可行性。此外，讨论结果表明，该方法对测点位置的依赖程度低，对冲击载荷的识别能力强，实时性好。该方法为高速多变工作环境下的宽频域接触力识别提供了一种新的有效解决方案。

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引用次数: 0