Mechanical Systems and Signal Processing最新文献_第8页

Efficient simulation of conditional random fields by Karhunen–Loève expansion karhunen - lo<e:1>展开对条件随机场的有效模拟

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-31 DOI: 10.1016/j.ymssp.2026.113938

Zhao Zhao , Teng-Fei Xu , Zhao-Hui Lu , Yan-Gang Zhao

The conditional random fields, by integrating on-site measured data information, provide a more practical and realistic tool for the engineering analysis of phenomena that exhibit random characteristics in both space and time across multiple dimensions. However, traditional simulation methods of conditional random fields still face significant computational bottlenecks when dealing with large-scale problems. To this end, this paper proposes a novel and efficient simulation technique for conditional random fields. The core of the proposed method lies in a refined approach to the Karhunen-Loève (K-L) expansion. Instead of approximating the full conditional covariance function, we directly compute or more accurately approximate the dominant eigenvalues and eigenfunctions of the theoretically exact conditional covariance function. This computation is achieved by using the Nyström approximation, conditional multivariate Gaussian distribution, and selected quadrature points. This streamlined process allows us to directly generate conditional random field realizations within the K-L expansion framework. The effectiveness and robustness of the proposed method are demonstrated through three numerical examples, including one-dimensional, two-dimensional, and large-scale three-dimensional conditional random field simulations. Results confirm that the proposed approach achieves an optimal balance between computational efficiency and simulation accuracy, providing a powerful tool for data-inform probabilistic engineering analysis.

条件随机场通过整合现场实测数据信息，为跨多维空间和时间随机性现象的工程分析提供了更为实用和现实的工具。然而，传统的条件随机场模拟方法在处理大规模问题时仍然面临着显著的计算瓶颈。为此，本文提出了一种新的、高效的条件随机场仿真技术。该方法的核心是对karhunen - lo (K-L)展开的一种改进方法。我们不是逼近完整的条件协方差函数，而是直接计算或更精确地逼近理论上精确的条件协方差函数的主导特征值和特征函数。这个计算是通过使用Nyström近似，条件多元高斯分布和选择的正交点来实现的。这个简化的过程允许我们在K-L扩展框架内直接生成条件随机场实现。通过一维、二维和大尺度三维条件随机场模拟，验证了该方法的有效性和鲁棒性。结果表明，该方法在计算效率和仿真精度之间达到了最佳平衡，为数据信息概率工程分析提供了有力的工具。

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引用次数: 0

A novel frequency-domain health indicator for bearing RUL estimation using adaptive Wiener process degradation modeling 基于自适应维纳过程退化模型的轴承RUL估计频域健康指标

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-31 DOI: 10.1016/j.ymssp.2026.113955

Afshin Nagheli, Mehrdad Poursina, Hossein Karimpour

Accurate and reliable estimation of bearing health conditions requires the construction of a suitable Health Indicator (HI). In this study, the Modified Total Harmonic Distribution (MTHD) health indicator is developed based on advanced frequency domain analysis to describe the bearing health status effectively. It has also been validated that MTHD demonstrated desirable properties of monotonicity, robustness, and trendability. To accurately identify the First Prediction Time (FPT), a linear combination of the mean and variance of the MTHD curve is employed. However, due to variations in operating conditions and loading, the degradation process of bearings may differ. As a result, a single fixed model cannot accurately characterize the occurrence of different degradation processes. To address this issue, an adaptive Wiener model is proposed. In this framework, the Remaining Useful Life (RUL) prediction is achieved using either an appropriate linear or nonlinear Wiener model selected through a model adaptive algorithm. Finally, the effectiveness of the proposed model is validated using the XJTU-SY bearing dataset as well as the laboratory’s own generated dataset.

准确可靠地估计轴承健康状况需要构建合适的健康指标（HI）。为了有效地描述轴承的健康状态，提出了基于先进频域分析的修正总谐波分布（MTHD）健康指标。结果表明，MTHD具有单调性、鲁棒性和趋势性。为了准确地识别第一次预测时间（FPT），采用了MTHD曲线均值和方差的线性组合。然而，由于操作条件和载荷的变化，轴承的退化过程可能不同。因此，单一的固定模型不能准确表征不同退化过程的发生。为了解决这一问题，提出了一种自适应维纳模型。在该框架中，剩余使用寿命（RUL）预测是通过模型自适应算法选择适当的线性或非线性维纳模型来实现的。最后，使用XJTU-SY轴承数据集以及实验室自己生成的数据集验证了所提出模型的有效性。

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引用次数: 0

Bias drift compensation of butterfly gyroscope under temperature change with two-stage hybrid deep learning model 基于两阶段混合深度学习模型的温度变化下蝶形陀螺仪偏置漂移补偿

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-31 DOI: 10.1016/j.ymssp.2026.113937

Gao Liu , Zhanqiang Hou , Xuan Wang , Mi Zeng , Xi Chen , Qingsong Li , Dingbang Xiao , Xuezhong Wu

This paper proposes a two-stage hybrid deep learning compensation strategy that integrates Bayesian optimization (BO), Transformer, and temporal convolutional network (TCN) to suppress bias drift in butterfly gyroscope under temperature change. First, a theoretical model for the butterfly gyroscope output was established, and the composition of thermally induced bias was analyzed. Subsequently, the original bias output signal from the gyroscope control system was decomposed using variational mode decomposition (VMD). Permutation entropy and Pearson correlation coefficient were employed as screening methods to extract the intrinsic mode functions (IMFs) caused by temperature change and strip noise. Then other temperature-related parameters that can be output serve as physically interpretable feature vector, which, together with the bias signal from the previous time step, constructs an enhanced input sequence that integrates historical memory. Subsequently, a two-stage compensation strategy was established: In the first stage, the Transformer model, which is equipped with self-attention mechanisms that capture long-range dependency, was then employed to model and compensate for deterministic drift within the reconstructed bias signal; In the second stage, the preliminarily compensated non-deterministic residual signal was fed into the TCN network, which has strong local modeling capabilities to further fit residual error, achieving precise compensation for local fluctuations. Finally, BO was employed for adaptive joint hyperparameter tuning across preprocessing and submodels. Experimental results demonstrate that the proposed two-stage hybrid compensation strategy achieves superior compensation accuracy, robustness, and generalization capability compared to traditional polynomial fitting methods and various single or combined deep learning models. Specifically, it improves the standard deviation of the compensated bias signal by over 38.3% and 42.8%, respectively, across different temperature change rates and gyroscope samples.

提出了一种结合贝叶斯优化（BO）、变压器（Transformer）和时间卷积网络（TCN）的两阶段混合深度学习补偿策略，以抑制温度变化下蝶形陀螺仪的偏置漂移。首先，建立了蝶形陀螺仪输出的理论模型，分析了其热致偏置的组成。然后，利用变分模态分解（VMD）对陀螺仪控制系统的原始偏置输出信号进行分解。采用排列熵和Pearson相关系数作为筛选方法，提取温度变化和条带噪声引起的本征模态函数（IMFs）。然后，可以输出的其他与温度相关的参数作为物理可解释的特征向量，与前一个时间步长的偏置信号一起构建一个集成历史记忆的增强输入序列。随后，建立了两阶段补偿策略：第一阶段，利用Transformer模型对重构偏置信号中的确定性漂移进行建模和补偿，该模型具有捕获远程依赖的自关注机制；第二阶段，将初步补偿的不确定性残差信号送入具有较强局部建模能力的TCN网络，进一步拟合残差误差，实现对局部波动的精确补偿。最后，利用模糊神经网络进行跨预处理和子模型的自适应联合超参数整定。实验结果表明，与传统的多项式拟合方法和各种单一或组合的深度学习模型相比，所提出的两阶段混合补偿策略具有更好的补偿精度、鲁棒性和泛化能力。具体来说，在不同温度变化率和陀螺仪样本下，补偿偏置信号的标准差分别提高了38.3%和42.8%以上。

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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 : 2026-01-29 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

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 : 2026-01-29 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

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 : 2026-01-29 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

Nonlinear dynamics of tristable galloping-based energy harvesters and their application in weak signal enhancement 三稳驰振能量采集器的非线性动力学及其在弱信号增强中的应用

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-29 DOI: 10.1016/j.ymssp.2026.113933

Haitao Xu , Shengxi Zhou

In nonlinear systems, the multistable potential function has been demonstrated to be an effective means to broaden the work frequency bandwidth. Taking the energy harvester as an example, the function can help it efficiently capture the broadband energy under low-level excitations. However, it is necessary to discuss the effect of asymmetry of the multistable potential function on nonlinear dynamics. Firstly, this paper designs the piecewise tristable potential function, which can produce three types of asymmetries. Secondly, according to tristable galloping-based energy harvesters, influence of asymmetries on their responses under harmonic excitation, and on the stochastic resonance phenomenon under harmonic and random excitations are investigated by numerical simulations. In addition, the phase trajectories, Poincaré maps and Lyapunov exponents are also employed to exam the system responses, such as the chaotic motion, quasi-periodic motion, and periodic motion. Thirdly, according to the experimental signal, the proposed signal enhancement methods based on the stochastic resonance of tristable galloping-based energy harvesters are successfully validated. The output signal-to-noise ratios are also calculated to compare their performance. Overall, this paper explores the effect of asymmetry on nonlinear dynamics, as well as the potential application in signal processing.

在非线性系统中，多稳定势函数已被证明是拓宽工作带宽的有效手段。以能量采集器为例，该函数可以有效地捕获低能级激励下的宽带能量。然而，有必要讨论多稳定势函数的不对称性对非线性动力学的影响。首先，本文设计了分段三稳定势函数，它可以产生三种不对称。其次，针对三稳驰振能量采集器，通过数值模拟研究了不对称性对其谐波激励下响应的影响，以及谐波和随机激励下随机共振现象的影响。此外，还采用相轨迹、poincar映射和Lyapunov指数来检测系统的混沌运动、准周期运动和周期运动等响应。第三，根据实验信号，成功验证了基于三稳驰骋能量采集器随机共振的信号增强方法。还计算了输出信噪比，以比较它们的性能。总的来说，本文探讨了不对称对非线性动力学的影响，以及在信号处理中的潜在应用。

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引用次数: 0

Anomalous data diagnosis in bridge strain monitoring by fusing multi-modal data feature 融合多模态数据特征的桥梁应变监测异常数据诊断

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-29 DOI: 10.1016/j.ymssp.2026.113922

Pengtao Chen , Gan Yang , Junfeng Wang , Xiuping Liu , Shizhi Chen , Wanshui Han

Identifying anomalous monitoring data caused by unstable sensor performance is crucial for accurately assessing the operational condition of bridges. In practical monitoring, such anomalies often exhibit various complex patterns, such as slow-varying trends and missing data. However, traditional analysis methods based on unimodal data features struggle to simultaneously consider both the transient dynamics and the global evolutionary features of time-series data, which leads to insufficient identification capability for slow-varying anomalies such as drift and trend. To address this, a framework for diagnosing anomalous bridge data based on multimodal data feature fusion is proposed, which achieves fine-grained identification of complex anomaly patterns by fusing Markov Transition Field (MTF) image features with one-dimensional (1D) time-series features. This fusion dynamically combines features from two parallel branches: one branch extracts global state transition patterns from the MTF images, while the other captures key transient dynamics from the 1D time-series data. Experimental results show that the method achieves an overall mean Average Precision (mAP) of 99.83% on the main girder strain monitoring data from a highway cable-stayed bridge (across seven data classes), representing a significant improvement compared to models using only unimodal data features, with the image-only model achieving 94.63% and the time-series-only model achieving 91.34%. Notably, the F1-Scores for minority slow-varying anomalies (trend, drift) are improved by over 15%. Furthermore, the model demonstrates strong generalization, achieving 97.97% accuracy on a large-scale dataset collected from sensor locations that were used during training.

识别由传感器性能不稳定引起的异常监测数据对于准确评估桥梁运行状况至关重要。在实际监测中，这种异常常常表现出各种复杂的模式，如缓慢变化的趋势和丢失的数据。然而，传统的基于单峰数据特征的分析方法难以同时考虑时间序列数据的瞬态动力学和全局演化特征，导致对漂移和趋势等慢变异常的识别能力不足。针对这一问题，提出了一种基于多模态数据特征融合的桥梁异常数据诊断框架，该框架通过将马尔可夫过渡场（MTF）图像特征与一维时间序列特征融合，实现了复杂异常模式的细粒度识别。这种融合动态地结合了两个并行分支的特征：一个分支从MTF图像中提取全局状态转移模式，而另一个分支从一维时间序列数据中捕获关键的瞬态动态。实验结果表明，该方法对某公路斜拉桥主梁应变监测数据（跨越7个数据类别）的总体平均平均精度（mAP）达到99.83%，与仅使用单峰数据特征的模型相比有显著提高，其中仅图像模型达到94.63%，仅时间序列模型达到91.34%。值得注意的是，少数慢变异常（趋势，漂移）的f1分数提高了15%以上。此外，该模型具有很强的泛化能力，在训练期间使用的传感器位置收集的大规模数据集上达到97.97%的准确率。

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引用次数: 0

A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses 基于自相关频率响应间残差估计聚合物波导弹性材料参数的改进Levenberg-Marquardt方法

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-29 DOI: 10.1016/j.ymssp.2026.113904

Dominik Itner , Dmitrij Dreiling , Hauke Gravenkamp , Bernd Henning , Carolin Birk

In this contribution, we address the estimation of the frequency-dependent elastic parameters of polymers in the ultrasound range, which is formulated as an inverse problem. This inverse problem is implemented as a nonlinear regression-type optimization problem, in which the simulation signals are fitted to the measurement signals. These signals consist of displacement responses in waveguides, focusing on hollow cylindrical geometries to enhance the simulation efficiency. To accelerate the optimization and reduce the number of model evaluations and wait times, we propose two novel methods. First, we introduce an adaptation of the Levenberg–Marquardt method derived from a geometrical interpretation of the least-squares optimization problem. Second, we introduce an improved objective function based on the autocorrelated envelopes of the measurement and simulation signals. Given that this study primarily relies on simulation data to quantify optimization convergence, we aggregate the expected ranges of realistic material parameters and derive their distributions to ensure the reproducibility of optimizations with proper measurements. We demonstrate the effectiveness of our objective function modification and step adaptation for various materials with isotropic material symmetry by comparing them with the Broyden–Fletcher–Goldfarb–Shanno method. In all cases, our method reduces the total number of model evaluations, thereby shortening the time to identify the material parameters.

在这一贡献中，我们解决了超声范围内聚合物的频率相关弹性参数的估计，这是一个逆问题。该反问题是一个非线性回归型优化问题，其中仿真信号拟合到测量信号。这些信号由波导中的位移响应组成，重点关注空心圆柱几何形状，以提高仿真效率。为了加快优化速度，减少模型评估的次数和等待时间，我们提出了两种新的方法。首先，我们从最小二乘优化问题的几何解释中引入了Levenberg-Marquardt方法的适应性。其次，基于测量信号和仿真信号的自相关包络引入了一种改进的目标函数。鉴于本研究主要依赖于模拟数据来量化优化收敛，我们汇总了实际材料参数的预期范围，并推导了它们的分布，以确保优化的可重复性。通过与Broyden-Fletcher-Goldfarb-Shanno方法的比较，证明了我们的目标函数修正和步进自适应方法对具有各向同性材料对称性的各种材料的有效性。在所有情况下，我们的方法减少了模型评估的总数，从而缩短了识别材料参数的时间。

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引用次数: 0

A comprehensive review of indirect bridge health monitoring 桥梁间接健康监测技术综述

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-29 DOI: 10.1016/j.ymssp.2026.113918

Zhenkun Li , Weiwei Lin , Chul-Woo Kim , Maria Pina Limongelli , Eleni Chatzi

Indirect Bridge Health Monitoring (BHM) using indirect measurements of the response from passing vehicles has recently gained significant attention from researchers within the Structural Health Monitoring (SHM) domain. This approach requires only one or a few sensors installed on the vehicle, making it more cost-effective, efficient, and easier to implement than traditional methods, which demand numerous sensors on bridges. Recent advancements in both algorithms and hardware have further accelerated progress in this field. This paper aims to provide a comprehensive, one-stop review of indirect BHM using measured vehicle response since 2004. It systematically analyzes the connections and integrations within existing literature, incorporating rapidly emerging state-of-the-art studies. The review initiates with a bibliometric analysis, covering annual publication trends, keyword cooccurrence, and authorship networks, followed by a discussion on the fundamental theories of vehicle–bridge interaction. Subsequently, it summarizes the vehicle, bridge, and road roughness models used in indirect BHM. Furthermore, it explores current techniques and challenges in identifying bridge modal parameters, such as bridge frequencies, mode shapes, and damping ratios, as well as in indirect bridge damage detection using signal processing, modal-based, and data-driven methods. Additionally, this review includes affiliated studies that, while not directly related, contribute to the advancement of indirect BHM. Finally, recent developments in 2025, future investigation directions, and key conclusions are provided. It is intended to serve as a fundamental resource for researchers seeking to advance their studies in the field of indirect BHM.

间接桥梁健康监测（BHM）是一种利用间接测量过往车辆对桥梁的反应的方法，近年来受到了结构健康监测（SHM）领域研究人员的广泛关注。这种方法只需要在车辆上安装一个或几个传感器，与需要在桥梁上安装多个传感器的传统方法相比，它更具成本效益、效率更高，也更容易实施。最近在算法和硬件方面的进步进一步加速了这一领域的进展。本文旨在提供一个全面的，一站式的间接BHM审查使用测量车辆响应自2004年以来。它系统地分析了现有文献中的联系和整合，结合了快速出现的最先进的研究。回顾从文献计量分析开始，包括年度出版趋势、关键词协同和作者网络，随后讨论了车桥相互作用的基本理论。随后，总结了间接BHM中使用的车辆、桥梁和道路粗糙度模型。此外，它还探讨了识别桥梁模态参数的当前技术和挑战，例如桥梁频率，模态振型和阻尼比，以及使用信号处理，基于模态和数据驱动方法的间接桥梁损伤检测。此外，本综述还包括了与间接BHM进展相关的附属研究，尽管这些研究没有直接关系。最后，给出了2025年的最新发展、未来的研究方向和关键结论。它旨在为寻求推进间接BHM领域研究的研究人员提供基础资源。

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引用次数: 0