Mechanical Systems and Signal Processing最新文献_第4页

Ultra-low frequency air flotation vibration isolation system with a dual-chamber structure using adaptive control strategy 采用自适应控制策略的双腔结构气浮超低频隔振系统

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 Epub Date: 2026-02-10 DOI: 10.1016/j.ymssp.2026.113987

Tianyi Li, Shilong Guo, Zhendong Lan, Bo Zhao, Jiubin Tan, Chenglong Yu

Vibration isolation systems for ultra-precision instruments are strongly influenced by internal resonances, leading to an increase in vibration transmissibility of up to 10–30 dB at the resonance frequencies. The dual-chamber air-floating vibration isolation system exhibits an extremely low natural frequency. However, the presence of the expansion chamber introduces internal resonance problems at mid-to-high frequencies. To enhance the vibration isolation performance of the dual-chamber air-floated isolation system, this paper proposes an adaptive control strategy tailored to such systems to address internal resonance beyond the natural frequency. The dual-chamber air-floated isolation system is accurately modeled and systematically analyzed in this paper. The results reveal that the fundamental cause of internal resonance in the dual-chamber isolation system is Helmholtz resonance. To address this issue, a novel orthogonal basis function infinite impulse response (OBF-IIR) controller is designed in this paper to efficiently compensate for vibrations induced by the dual-chamber Helmholtz resonance effect. On this basis, a fast, accurate online adaptive algorithm is developed to update the controller zeros in real time, enabling adaptive, synchronous compensation of internal resonances in the dual-chamber isolation system. The proposed OBF-IIR controller not only suppresses internal resonances induced by the spring–damper model and the dual-chamber Helmholtz resonance effect, but also compensates for resonances arising from other sources. The proposed adaptive control strategy demonstrates faster convergence and higher accuracy, reducing the vibration transmissibility of the isolation system by 10–30 dB in the 2–100 Hz range and decreasing the cumulative power spectral density at 100 Hz by 23.8%–84.9%.

超精密仪器的隔振系统受内部共振的强烈影响，导致共振频率下的振动透射率增加高达10-30 dB。双腔气浮隔振系统具有极低的固有频率。然而，膨胀室的存在在中高频引入了内部共振问题。为了提高双腔气浮隔振系统的隔振性能，本文提出了一种针对双腔气浮隔振系统的自适应控制策略，以解决固有频率以外的内部共振问题。本文对双腔气浮隔离系统进行了精确的建模和系统的分析。结果表明，双腔隔离系统内部共振的根本原因是亥姆霍兹共振。为了解决这一问题，本文设计了一种新的正交基函数无限脉冲响应（OBF-IIR）控制器，以有效补偿双腔亥姆霍兹共振效应引起的振动。在此基础上，开发了一种快速、准确的在线自适应算法，实时更新控制器零点，实现双腔隔离系统内部谐振的自适应、同步补偿。所提出的OBF-IIR控制器不仅可以抑制由弹簧-阻尼器模型和双腔Helmholtz共振效应引起的内部共振，还可以补偿其他来源引起的共振。所提出的自适应控制策略收敛速度快、精度高，使隔振系统在2 ~ 100 Hz范围内的振动传递率降低了10 ~ 30 dB， 100 Hz范围内的累计功率谱密度降低了23.8% ~ 84.9%。

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引用次数: 0

A dynamic model for instantaneous angular speed jitter in low-speed rolling element bearing to mimic natural spalling zone on outer race 模拟外圈自然剥落区低速滚动轴承瞬时角速度抖动的动态模型

IF 8.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 DOI: 10.1016/j.ymssp.2026.114143

Qiao Han, Yu Guo, Jiawei Fan, Jing Na, Xingchao Yin

The instantaneous angular speed (IAS) is well suited for diagnosing bearing faults in low-speed conditions because it is synchronized with shaft rotation and can reveal the dynamic behavior of rollers when they enter and leave a spalling zone. Prior studies have emphasized vibration responses at medium and high speeds and often idealized spalling as rectangular notches, which overlooks natural spalling morphology and the torque produced by rough surfaces. We develop a dynamic IAS model for rolling element bearings (REBs) with a spalling outer race (OR) that combines a displacement-gradient representation of natural spalling with a time-varying additional torque and friction model, and that accounts for stiffness variation as well as entry and exit excitations. Simulations and experiments validate the model and explain IAS jitter near the natural spalling zone at low speed. The approach enables estimation of spall width on the OR. The results clarify the mechanism of IAS jitter under realistic operating conditions and provide guidance for dynamic modeling methods that use IAS.

瞬时角速度（IAS）非常适合诊断低速条件下的轴承故障，因为它与轴的旋转同步，可以显示滚子进入和离开剥落区时的动态行为。先前的研究强调中高速下的振动响应，通常将剥落理想化为矩形缺口，忽略了自然剥落形态和粗糙表面产生的扭矩。我们为带有剥落外圈（OR）的滚动轴承（reb）开发了一个动态IAS模型，该模型将自然剥落的位移梯度表示与时变附加扭矩和摩擦模型相结合，并考虑了刚度变化以及进入和退出激励。仿真和实验验证了模型的正确性，并解释了低速下IAS在自然剥落区附近的抖动。该方法能够在OR上估计小波宽度。研究结果阐明了实际工况下IAS抖动的机理，为采用IAS的动态建模方法提供了指导。

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引用次数: 0

Asymmetric design enables self-coupled locally resonant metastructure for multi-modal vibration isolation 非对称设计使自耦合局部谐振元结构能够实现多模态隔振

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 Epub Date: 2026-02-10 DOI: 10.1016/j.ymssp.2026.113984

Xiaowei Zhang, Xiaopeng Wang, Yingrui Ye

Symmetry is commonly used in engineering design for its simplicity and structural stability. Conventional locally resonant metastructures with strict spatial symmetry exhibit only one active mode, limiting modal diversity and dynamic performance. To overcome this constraint, we introduce spatial stiffness asymmetry, enabling three-dimensional dynamic responses. Such asymmetric design induces coupling between translational and rotational degrees of freedom, allowing multiple resonant modes to be excited by a single-directional input. Leveraging this mechanism, we design a metastructure that achieves vertical vibration isolation through three distinct coupled modes generated by a single resonator. A theoretical model is developed to describe the asymmetric self-coupling behavior, and vibration-table experiments confirm the predicted multi-band isolation performance. This work provides a new strategy for enhancing modal utilization in resonant systems and offers practical guidance for compact, multi-band vibration control.

对称因其简单和结构稳定而被广泛应用于工程设计中。传统的局部共振元结构具有严格的空间对称性，只有一个主动模态，限制了模态多样性和动态性能。为了克服这一限制，我们引入了空间刚度不对称，实现了三维动态响应。这种不对称设计诱导了平移自由度和旋转自由度之间的耦合，允许通过单向输入激发多个谐振模式。利用这一机制，我们设计了一种元结构，通过单个谐振器产生的三种不同的耦合模式实现垂直隔振。建立了描述非对称自耦合行为的理论模型，振动台实验证实了所预测的多波段隔离性能。这项工作为提高谐振系统的模态利用率提供了新的策略，并为紧凑的多频段振动控制提供了实用指导。

引用次数: 0

Physics-informed neural networks based digital volume correlation for displacement and strain measurements 基于物理信息的数字体积相关的位移和应变测量神经网络

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 Epub Date: 2026-02-11 DOI: 10.1016/j.ymssp.2026.113998

Zhuhong Wang, Hang Zhou, Hanlong Liu

Accurate measurement of three-dimensional deformation behavior is critical for understanding material mechanical properties. However, traditional Digital Volume Correlation (DVC) methods are limited by discrete sub-volume discretization, lack of physical constraints, and low computational efficiency. Data-driven approaches cannot guarantee physical plausibility and depend on large quantities of densely sampled data. This study proposes a novel physics-informed deep learning method for DVC (PiNetDVC). The method takes spatial coordinates as inputs and simultaneously predicts displacement and strain fields through continuous function representation, overcoming spatial resolution limitations and data dependency. The strain field is directly incorporated as a network output, with strain–displacement compatibility enforced by comparing network-predicted strains with strains derived from displacement gradients. A unified loss function integrates image consistency constraints with physics-informed regularization. Validation on six scenarios demonstrates superior performance over traditional ALDVC, achieving accuracy improvements of 81%, 83%, and over 95% for rigid body translation, uniaxial tension, and shear band deformation, respectively. For complex deformation patterns such as sinusoidal and non-uniform star-shaped modes, errors are maintained at the order of 10^-3. Stable accuracy is maintained under 20 dB noise, with robust performance across different architectures and loss configurations. PiNetDVC provides an effective solution for 3D deformation measurement in aerospace, mechanical, and civil engineering applications.

三维变形行为的精确测量是理解材料力学性能的关键。然而，传统的数字体积相关（DVC）方法受到离散子体积离散化、缺乏物理约束和计算效率低等限制。数据驱动的方法不能保证物理上的合理性，并且依赖于大量密集采样的数据。本研究提出了一种新的基于物理的DVC深度学习方法（PiNetDVC）。该方法以空间坐标为输入，通过连续函数表示同时预测位移场和应变场，克服了空间分辨率的限制和数据依赖性。应变场直接作为网络输出，通过比较网络预测的应变与由位移梯度得出的应变来实现应变-位移相容。统一的损失函数集成了图像一致性约束和物理信息正则化。在六种情况下的验证表明，该方法的性能优于传统的ALDVC，在刚体平移、单轴拉伸和剪切带变形方面的精度分别提高了81%、83%和95%以上。对于复杂的变形模式，如正弦和非均匀星形模式，误差保持在10-3的数量级。在20 dB噪声下保持稳定的精度，在不同的架构和损耗配置下具有强大的性能。PiNetDVC为航空航天、机械和土木工程应用中的三维变形测量提供了有效的解决方案。

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引用次数: 0

Accelerated alternating iterative identification for multiple moving vehicle loads based on Anderson acceleration with safeguard strategy 基于Anderson加速度和保障策略的多运动车辆荷载加速交替迭代辨识

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 Epub Date: 2026-02-11 DOI: 10.1016/j.ymssp.2026.113979

Bohao Xu, Ling Yu, Zhenhua Nie

As one of the challenging topics in structural health monitoring, the identification of multiple moving vehicle loads remains largely unexplored owing to the large differences in load magnitudes. Even though a recent study introduced multiple regularization parameters (MRP) within a two-stage framework to distinguish the properties of different loads, its performance is highly sensitive to the initial estimates and deteriorates as the number of loads increases. To address this, the original two-stage work is extended into an alternating iterative framework (AIF), which iteratively updates the static load, dynamic load, and the variance of the dynamic loads. This extension follows the conclusion in the previous study that the regularization parameters chosen within the reasonable range of residual noise are close. Furthermore, Anderson acceleration is introduced only to the static load and the variance of dynamic load to enhance effectiveness. A safeguard strategy is incorporated to ensure the local convergence of the AIF. Finally, the proposed method is validated in both numerical simulations and laboratory experiments. The comparative cases under different response combinations, different numbers of loads and different initial estimates in the numerical simulations show that the proposed method achieves a higher accuracy, especially in comparison with the previous study. The SNR threshold required for maintaining reliable identification decreases from 25 dB to 20 dB, even when the noise variance is inaccurately estimated. Moreover, the weight of the model vehicle can be reasonably estimated by the proposed method in the validation of experiment.

作为结构健康监测中具有挑战性的课题之一，由于荷载大小差异较大，多运动车辆荷载的识别在很大程度上尚未得到探索。尽管最近的一项研究在两阶段框架中引入了多个正则化参数（MRP）来区分不同载荷的特性，但其性能对初始估计高度敏感，并且随着载荷数量的增加而恶化。为了解决这个问题，最初的两阶段工作被扩展为交替迭代框架（AIF），它迭代地更新静态负载、动态负载和动态负载的变化。这一扩展是基于之前研究的结论，即在残余噪声的合理范围内选择的正则化参数是接近的。此外，为了提高有效性，只对静载荷和动载荷的变化引入了安德森加速度。纳入保障策略以确保AIF的局部收敛。最后，通过数值模拟和室内实验验证了该方法的有效性。数值模拟中不同响应组合、不同荷载数和不同初始估计下的对比实例表明，该方法具有较高的精度，特别是与前人的研究结果相比。维持可靠识别所需的信噪比阈值从25 dB降低到20 dB，即使在不准确估计噪声方差的情况下也是如此。实验验证表明，该方法能合理估算模型车的重量。

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引用次数: 0

A data-mechanism-based digital twin system for intelligent contour error compensation of ultra-precision machining 一种基于数据机制的超精密加工轮廓误差智能补偿数字孪生系统

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-15 Epub Date: 2026-02-11 DOI: 10.1016/j.ymssp.2026.113982

Chengyi Wu , Shijun Ji , Ji Zhao , Enzhong Zhang , Guang Yang

In the implementation of digital twin for ultra-precision machining (UPM) based on deep learning, conventional approaches suffer from limited interpretability of model and insufficient visualization capabilities. Moreover, their performance is significantly compromised by the coupling effects of multisource errors, making it difficult to achieve accurate position prediction and effective compensation. To address these limitations, this paper proposes a novel digital twin system which is driven by a hybrid model that integrates the Patch Time Series Transformer and multisource error coupling mechanism, and enables the visualization of the error compensation strategy. It achieves intelligent contour error compensation during machining by dynamically correcting the position commands along the trajectory. Based on an analysis of the theoretical error band arising from the multisource error coupling mechanism, the position prediction accuracy of each axis is improved through the self-supervised learning and hyperparameter fine-tuning methods. Furthermore, temporal stability is validated via time-effect analysis. Comprehensive case studies are conducted on a custom-built multi-axis ultra-precision machine tool, covering both single-axis and multi-axis motions under varying loads, feedrates, and ambient temperatures. The test results demonstrate that the proposed method improves single-axis positioning accuracy by 47.07% and multi-axis trajectory contour accuracy by 26.99%. In the micro-groove machining experiment, the compensated linear positioning error is reduced to 0.0393 μm, and the angular positioning error is 0.0013°, with the resultant cutting force indirectly reduced by up to 9.20%. The robustness and adaptability of the proposed method are validated under complex operating conditions, thereby enabling high-accuracy contour control in practical UPM applications.

在基于深度学习的数字孪生超精密加工（UPM）实现中，传统方法存在模型可解释性有限和可视化能力不足的问题。此外，多源误差的耦合影响会严重影响其性能，难以实现准确的位置预测和有效的补偿。为了解决这些限制，本文提出了一种新的数字孪生系统，该系统由集成了贴片时间序列变压器和多源误差耦合机制的混合模型驱动，并实现了误差补偿策略的可视化。通过沿轨迹动态修正位置指令，实现加工过程中轮廓误差的智能补偿。在分析多源误差耦合机制产生的理论误差带的基础上，通过自监督学习和超参数微调方法提高了各轴的位置预测精度。此外，通过时间效应分析验证了系统的时间稳定性。在定制的多轴超精密机床上进行了全面的案例研究，涵盖了不同负载、进给速度和环境温度下的单轴和多轴运动。实验结果表明，该方法可将单轴定位精度提高47.07%，将多轴轨迹轮廓精度提高26.99%。在微槽加工实验中，补偿后的直线定位误差减小到0.0393 μm，角定位误差减小到0.0013°，间接合成切削力减小了9.20%。在复杂操作条件下验证了该方法的鲁棒性和适应性，从而实现了UPM实际应用中的高精度轮廓控制。

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引用次数: 0

Corrigendum to “FD-MVLLM: Fault diagnosis based on multimodal vibration data and large language model for bearing system” [Mech. Syst. Sig. Process. 239 (2025) 113226] “FD-MVLLM：基于多模态振动数据和大语言模型的轴承系统故障诊断”的勘误[m]。系统。Sig. Process. 239 (2025) 113226]

IF 8.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-03-10 DOI: 10.1016/j.ymssp.2026.114094

Dayang Li, Zhibo Pang, Yongxu Chen, Kun Yang, Jinyan Shao, Yichen Luo, Yanghang Zeng, Chen He, Yutong Gao

引用次数: 0

FRF-based crack localization in AMB-Supported rotors using neural networks 基于频响函数的amb转子裂纹定位神经网络

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.2026.113939

Giovanni Donati , Chiara Camerota , Marco Mugnaini , Michele Basso , Jerzy T. Sawicki

Well-established procedures exist for monitoring and diagnosing faults in rotating machinery, and many techniques for detecting rotor cracks have been explored in the literature. However, limited progress has been made in developing non-invasive methods capable of accurately localizing rotor cracks and assessing their severity without requiring rotor disassembly or direct physical inspection.

This paper presents a novel, non-invasive approach for crack localization in flexible rotors supported by Active Magnetic Bearings (AMBs), based exclusively on frequency responses acquired through AMB excitation. The methodology involves constructing a physics-informed fault dictionary using frequency responses simulated on a high-fidelity digital twin of the rotor system, obtained through established modeling procedures, under various crack locations and severities. These responses exhibit characteristic shifts in resonance and antiresonance frequencies, which are used to define distinct fault classes.

Neural network classifiers were trained on the simulated dataset, with a 1D Convolutional Neural Network (1D-CNN) used as the primary model and an Autoencoder + Multilayer Perceptron (AE + MLP) used as a comparative baseline, to evaluate their ability to automatically identify the fault zone. The entire framework was validated experimentally on a dedicated AMB-supported test rig, confirming the ability of the proposed method to detect and localize cracks without requiring additional sensors or plant disassembly. The 1D-CNN achieved a classification accuracy of 99.4% on simulated test data, while the AE + MLP baseline reached 98.3%. Experimental validation on a dedicated AMB-supported test rig showed correct localization for all tested crack cases.

在旋转机械中存在着完善的监测和诊断故障的程序，并且在文献中探索了许多检测转子裂纹的技术。然而，在开发能够准确定位转子裂纹并评估其严重程度而无需拆卸转子或直接物理检查的非侵入性方法方面取得了有限的进展。本文提出了一种新颖的、非侵入式的基于主动磁轴承（AMBs）激励获得的频率响应的柔性转子裂纹定位方法。该方法包括通过建立的建模程序，在不同裂纹位置和严重程度下，通过在转子系统的高保真数字孪生上模拟频率响应，构建一个物理信息故障字典。这些响应表现出共振和反共振频率的特征移位，用于定义不同的故障类别。在模拟数据集上训练神经网络分类器，以1D卷积神经网络（1D- cnn）作为主要模型，以Autoencoder + Multilayer Perceptron （AE + MLP）作为比较基线，评估其自动识别断裂带的能力。整个框架在专用的amb测试平台上进行了实验验证，证实了所提出的方法能够在不需要额外传感器或拆卸设备的情况下检测和定位裂缝。1D-CNN在模拟测试数据上的分类准确率达到99.4%，AE + MLP基线达到98.3%。在amb支持的专用测试台上进行的实验验证表明，所有测试的裂纹情况都是正确的定位。

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引用次数: 0

Model updating method based on computer vision and autocorrelation sensitivity: Deep integration of visual information and physical mechanisms 基于计算机视觉和自相关敏感性的模型更新方法：视觉信息与物理机制的深度融合

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.2026.113964

Weijia Liu , Changhai Zhai , Weiping Wen , Kun Liu

Traditional structural health monitoring relies on sensor deployment but is constrained by high installation costs, insufficient monitoring network coverage, and noise interference in complex seismic scenarios, limiting its application. Leveraging existing surveillance cameras in buildings for non-contact monitoring emerges as a promising solution. This study proposes a finite element model updating method integrating computer vision with time-domain signal autocorrelation sensitivity. This method deeply integrates visual displacement data from surveillance videos with structural mechanics models, employing the autocorrelation function of time-domain signals for effective noise reduction. It enhances the identification of local stiffness changes, thereby significantly improving the accuracy and robustness of model updating. This study first conducts model updating through numerical simulation methods. The displacement autocorrelation sensitivity method is employed, systematically accounting for measured response noise, seismic motion noise, and uncertainties in seismic motion (including spectral characteristics, duration, and peak ground acceleration). Numerical simulation results demonstrate that, under structural response and seismic motion noise conditions with a signal-to-noise ratio (SNR) as low as 20 dB, the displacement autocorrelation sensitivity method achieves a parameter updating error within 5%, validating its high adaptability and robustness in complex disturbance environments. For far-field non-impulsive seismic motions, the displacement autocorrelation sensitivity method exhibits higher precision and stability compared to traditional displacement sensitivity methods. For engineering feasibility assessment, shaking table tests were conducted on a three-story steel frame, integrating displacement time histories from indoor/outdoor camera videos with ground motion data from IMU sensors for model updating. Test results show Pearson correlation coefficients of 0.91, 0.94, and 0.97 for displacement time history predictions versus measured values from the top to the first story, with peak displacement relative errors below 6% for all stories. This method can efficiently utilize existing building surveillance videos to complete model updates within minutes in post-earthquake environment, providing reliable support for damage assessment and emergency response.

传统的结构健康监测依赖于传感器的部署，但受安装成本高、监测网络覆盖不足以及复杂地震场景下的噪声干扰等限制，限制了其应用。利用建筑物中现有的监控摄像头进行非接触式监控是一种很有前途的解决方案。提出了一种将计算机视觉与时域信号自相关灵敏度相结合的有限元模型更新方法。该方法将监控视频中的视觉位移数据与结构力学模型深度融合，利用时域信号的自相关函数进行有效降噪。增强了对局部刚度变化的识别，从而显著提高了模型更新的准确性和鲁棒性。本研究首先通过数值模拟方法进行模型更新。采用位移自相关灵敏度法，系统地考虑了实测响应噪声、地震运动噪声和地震运动中的不确定性（包括频谱特征、持续时间和峰值地面加速度）。数值模拟结果表明，在信噪比低至20 dB的结构响应和地震运动噪声条件下，位移自相关灵敏度方法的参数更新误差在5%以内，验证了其在复杂扰动环境下的高适应性和鲁棒性。对于远场非脉冲地震运动，位移自相关灵敏度方法比传统的位移灵敏度方法具有更高的精度和稳定性。为了进行工程可行性评估，在一个三层钢框架上进行了振动台试验，将室内外摄像机视频的位移时程与IMU传感器的地面运动数据相结合，用于模型更新。测试结果显示，位移时间历史预测与从顶层到第一层的测量值的Pearson相关系数分别为0.91、0.94和0.97，所有楼层的峰值位移相对误差均低于6%。该方法可以有效地利用现有建筑监控视频，在震后环境下几分钟内完成模型更新，为灾害评估和应急响应提供可靠的支持。

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引用次数: 0

Enhancing the prediction of squeal through multiphysic and multiscale analysis of experimental data from a pin-on-disk system 通过对针盘系统实验数据的多物理场和多尺度分析，增强了对尖叫的预测

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.2026.113968

Sacha Durain , Quentin Caradec , Mathis Briatte , Jean-François Brunel , Cédric Hubert , Franck Massa , Philippe Dufrenoy

Brake squeal remains a major challenge for the automotive industry due to its transient nature and its multiphysics, multiscale characteristics. This study advances the understanding and prediction of these dynamic instabilities through experimental analysis using an instrumented pin-on-disk system. The proposed multiphysics and multiscale framework reveals a correlation between the onset of dynamic instability and contact localization during a friction test. The results indicate that contact localization shaped by wear history and thermomechanical evolution plays a key role in triggering the instability.

由于制动啸叫的瞬态特性和多物理场、多尺度特性，一直是汽车工业面临的主要挑战。本研究通过使用仪器化针盘系统的实验分析，提高了对这些动态不稳定性的理解和预测。提出的多物理场和多尺度框架揭示了摩擦试验中动态不稳定性的发生与接触局部化之间的相关性。结果表明，磨损历史和热力学演化形成的接触局部化是引发失稳的关键因素。

引用次数: 0