Mechanical Systems and Signal Processing最新文献_第9页

Repetitive error selected iterative learning contouring control for precision multiaxis systems 精密多轴系统的重复误差选择迭代学习轮廓控制

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.2025.113583

Ze Wang , Fang Peng , Min Li , Taotao Chen , Chuxiong Hu , Yu Zhu

The contour error control method based on Iterative Learning Control (ILC) has gained widespread application in multi-axis precision motion systems due to its excellent control accuracy. Fundamentally, ILC works by filtering both repetitive and non-repetitive errors over multiple repetitive control tasks, and iteratively compensating for the repetitive errors to achieve extremely high control precision. In engineering applications, the low-frequency components of errors are typically considered repetitive, while the high-frequency components are regarded as random and non-repetitive. Based on this, ILC often utilizes low-pass filters to filter out repetitive errors. However, the causes of multi-axis contour errors are far more complex than those of single-axis tracking errors. We believe that directly using frequency characteristics to distinguish whether contour errors are repetitive is insufficiently accurate, despite the extensive use of this approach in prior studies. Therefore, this paper proposes a novel ILC method for contour error control based on the selection of repetitive errors. The method determines whether the errors in corresponding frequency bands exhibit repetitive characteristics based on the spectral features of the contour errors, thus enabling more precise filtering of repetitive errors. This approach effectively avoids the issues of convergence speed and precision degradation caused by the introduction of non-repetitive components during the iteration process in traditional ILC. Furthermore, we conducted a series of validation experiments on a multi-axis motion platform, which fully demonstrate that the proposed method outperforms traditional methods under various experimental conditions, effectively addressing the shortcomings of ILC in error filtering.

基于迭代学习控制（ILC）的轮廓误差控制方法以其优异的控制精度在多轴精密运动系统中得到了广泛的应用。从根本上说，ILC的工作原理是在多个重复控制任务上过滤重复和非重复误差，并迭代地补偿重复误差，以实现极高的控制精度。在工程应用中，误差的低频分量通常被认为是重复的，而高频分量则被认为是随机的和非重复的。基于此，ILC通常使用低通滤波器滤除重复误差。然而，多轴轮廓误差产生的原因远比单轴跟踪误差产生的原因复杂。我们认为，尽管在先前的研究中广泛使用这种方法，但直接使用频率特性来区分轮廓误差是否重复是不够准确的。因此，本文提出了一种基于重复误差选择的轮廓误差控制方法。该方法根据轮廓误差的频谱特征判断相应频带的误差是否具有重复特征，从而能够更精确地滤波重复误差。该方法有效地避免了传统ILC在迭代过程中由于引入非重复元件而导致的收敛速度和精度下降问题。此外，我们在多轴运动平台上进行了一系列验证实验，充分证明了该方法在各种实验条件下都优于传统方法，有效地解决了ILC在误差滤波方面的不足。

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引用次数: 0

A vision-based vibration measurement method of bridge structure using swin transformer motion magnification and improved recurrent all-pairs field transform algorithms 基于旋转变压器运动放大和改进的循环全对场变换算法的桥梁结构视觉振动测量方法

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.2026.113940

Jieqi Li , Wei Ji , Lu Deng , Yong Liu

Accurate measurement of bridge vibrations under load excitation is often impeded using traditional computer vision-based measurement (CVBM) methods in complex environments, particularly when small amplitudes and low-texture surfaces are involved. This paper proposes a CVBM method that estimates the full-field dynamic displacement and modal parameters of bridge structures by integrating the swin transformer motion magnification (STMM) with the improved recurrent all-pairs field transform (IRAFT) algorithm. The STMM algorithm could capture high-frequency information about structural vibrations while effectively suppressing noise, blurring, and motion artifacts during magnification. The IRAFT algorithm could improve the calculation accuracy of full-field optical flow in low-texture and large-motion scenes. First, vibrational videos of bridge structures are acquired and calibrated, and then the pixel motion in the video is magnified by the STMM algorithm. Subsequently, the IRAFT algorithm is employed to compute full-field optical flow from the magnified video. Finally, pixel motion within regions of interest is converted to a displacement time-history curve through motion normalization and a scale-factor method, from which modal parameters are identified based on fast Fourier transform and covariance-driven stochastic subspace identification. The proposed method was validated on a synthetic truss bridge, a laboratory experiment of a Q235 simply-supported beam, and on-site inspection of a pedestrian overpass. The results show that the utilization of the STMM algorithm is not only effective in acquiring high-frequency information of structural vibrations but also has the advantage of suppressing motion noise and artifacts during magnification. By integrating the STMM with the IRAFT algorithm, the identification performance of the full-field dynamic displacement and modal parameters of structures is significantly improved, with better robustness to illumination changes and partial occlusion.

传统的基于计算机视觉的测量（CVBM）方法在复杂环境下，特别是在涉及小振幅和低纹理表面时，往往无法准确测量荷载激励下的桥梁振动。本文提出了一种CVBM方法，通过将swin变压器运动放大（STMM）与改进的循环全对场变换（IRAFT）算法相结合，估计桥梁结构的全场动态位移和模态参数。STMM算法可以捕获结构振动的高频信息，同时有效地抑制放大过程中的噪声、模糊和运动伪影。IRAFT算法可以提高低纹理大运动场景下的全场光流计算精度。首先对桥梁结构的振动视频进行采集和标定，然后利用STMM算法对视频中的像素运动进行放大。随后，利用IRAFT算法计算放大后的视频的全场光流。最后，通过运动归一化和尺度因子方法将感兴趣区域内的像素运动转换为位移时程曲线，并基于快速傅立叶变换和协方差驱动的随机子空间识别方法识别模态参数。通过综合桁架桥、Q235简支梁的室内试验和人行天桥的现场检验，验证了该方法的有效性。结果表明，利用STMM算法不仅可以有效地获取结构振动的高频信息，而且在放大过程中具有抑制运动噪声和伪影的优点。通过将STMM与IRAFT算法相结合，显著提高了结构的全场动态位移和模态参数识别性能，对光照变化和局部遮挡具有更好的鲁棒性。

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引用次数: 0

A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis 基于多模态信息异构融合的液体脉冲神经网络模型用于可解释旋转机械故障诊断

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113923

Keshun You , Yingkui Gu , Haidong Shao , Yajun Wang

For the problems of dynamic feature attenuation, low efficiency of multimodal fusion and insufficient diagnostic interpretability in rotating machinery fault diagnosis, this paper proposes an interpretable multimodal heterogeneous fusion liquid impulse neural network (LINN) model. First, a liquid state coding layer based on differential equations is constructed to model the time-series dynamic evolution features in non-stationary signals via a chunked feedback mechanism. Moreover, a multi-channel leaky integrate-and-fire (MC-LIF) impulse neurons are introduced to enhance the extraction of transient shock features by combining alternative gradient and membrane potential attenuation strategies. Finally, an attention-guided multimodal fusion mechanism is designed to realize adaptive integration and contribution interpretability quantification of time–frequency features. In the high-noise and variable-load condition tests, LINN achieves more than 98.7% accuracy with only 4.1 M parameters and 88.64% cross-condition generalization accuracy. The ablation experiments verify the key role of liquid layer and impulse mechanism in enhancing dynamic modelling and noise immunity, and the interpretability analysis based on time–frequency domain attention (TFDA) further reveals the sensitive response of the model to key time–frequency modal contributions. The method provides an effective solution with high accuracy, strong generalization and interpretability for intelligent diagnosis under complex working conditions.

针对旋转机械故障诊断中存在的动态特征衰减、多模态融合效率低、诊断可解释性不足等问题，提出了一种可解释的多模态异构融合液体脉冲神经网络（LINN）模型。首先，构建基于微分方程的液相编码层，通过分块反馈机制对非平稳信号的时间序列动态演化特征进行建模；此外，我们还引入了一个多通道的MC-LIF脉冲神经元，通过结合替代梯度和膜电位衰减策略来增强瞬态冲击特征的提取。最后，设计了一种注意力引导的多模态融合机制，实现了时频特征的自适应融合和贡献可解释性量化。在高噪声、变负荷工况测试中，LINN仅使用4.1 M个参数，准确率达到98.7%以上，交叉条件泛化准确率达到88.64%。烧蚀实验验证了液体层和脉冲机制在增强动态建模和抗噪声方面的关键作用，基于时频域注意（TFDA）的可解释性分析进一步揭示了模型对关键时频模态贡献的敏感响应。该方法为复杂工况下的智能诊断提供了精度高、通用性强、可解释性好的有效解决方案。

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引用次数: 0

A new method for quantitative evaluation of micro-cracks on turbine blade surfaces Fusing triboelectric sensing and hybrid deep learning 基于摩擦电传感和混合深度学习的涡轮叶片表面微裂纹定量评估新方法

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113947

Jianfeng Tang , Yinglong Shang , Jiajian Meng , Junrong Li , Mingxu Xu , Yong Hu , Jianhai Zhang

Surface micro-cracks, a common and severe processing defect in CNC milling of high-performance components such as aero-engine turbine blades, act as stress concentration sites that drastically reduce the fatigue life of the component and threaten the structural safety of the engine. However, existing non-destructive testing techniques face challenges in being integrated into machine tools to achieve rapid, online, and quantitative in-situ detection of micro-defects on complex curved components. To address this, this paper proposes a novel in-situ detection paradigm based on a triboelectric nanogenerator (TENG), aiming to achieve high-precision quantitative inversion of the geometric parameters of micro-cracks on machined blade surfaces. The influence mechanism of crack width and depth on pulse amplitude and width was explained through systematic experimental research. Furthermore, by constructing multidimensional signal features that integrate time-domain and frequency-domain features, the coupling effects of width and depth can be decoupled. To achieve automated recognition, we constructed a hybrid deep learning model based on CNN-BiLSTM, which can autonomously mine the intrinsic correlation between spatiotemporal features and crack geometry parameters in the original signal, thereby synchronously outputting accurate predictions of width and depth. The experimental results show that the average absolute error of the model in predicting the width and depth of microcracks is as low as 0.0091 mm and 0.0047 mm, respectively, and the coefficient of determination (R²) is higher than 0.989. Furthermore, the potential of this method for identifying defects such as pitting, linear cracks, and network cracks has been further confirmed. This study not only confirms the enormous potential of TENG in intelligent manufacturing quality online monitoring, but also provides a solid technical path for the development of the next generation of intelligent integrated detection systems.

表面微裂纹是航空发动机涡轮叶片等高性能部件数控铣削加工中常见且严重的加工缺陷，其应力集中部位会大大降低部件的疲劳寿命，威胁发动机的结构安全。然而，现有的无损检测技术在集成到机床中以实现复杂曲面部件微缺陷的快速、在线和定量原位检测方面面临挑战。为了解决这一问题，本文提出了一种基于摩擦电纳米发电机（TENG）的新型原位检测范式，旨在实现叶片加工表面微裂纹几何参数的高精度定量反演。通过系统的试验研究，解释了裂纹宽度和深度对脉冲振幅和宽度的影响机理。此外，通过构建时域和频域特征相结合的多维信号特征，可以解耦宽度和深度的耦合效应。为了实现自动识别，我们构建了基于CNN-BiLSTM的混合深度学习模型，该模型可以自主挖掘原始信号中时空特征与裂纹几何参数之间的内在相关性，从而同步输出准确的宽度和深度预测。实验结果表明，该模型预测微裂纹宽度和深度的平均绝对误差分别低至0.0091 mm和0.0047 mm，决定系数（R2）均大于0.989。此外，进一步证实了该方法在识别点蚀、线状裂纹和网状裂纹等缺陷方面的潜力。本研究不仅证实了TENG在智能制造质量在线监测方面的巨大潜力，也为下一代智能集成检测系统的发展提供了坚实的技术路径。

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引用次数: 0

Energy difference of dynamic displacement curvature with frequency band selection for damage identification in bridges 基于频率选择的桥梁动位移曲率能量差损伤识别

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113945

Hai-nan Guo, Hao-xiang He, Xiao-jian Gao, Xi-hang Han

Due to long-term load, environmental erosion and natural factors, bridge is prone to damage and degradation. As the key goal of structural health monitoring, damage identification is particularly important. To address the challenges of complex calculations, insufficient accuracy, and high sensor requirements in traditional curvature methods, a novel method based on the dynamic displacement curvature energy difference is proposed. First, through Euler-Bernoulli beam theory and frequency response function analysis, an analytical relationship between displacement responses in different frequency ranges and mode shapes is derived. It shows that displacement responses near the natural frequency are effective for identifying damage. Then, to simplify calculations and minimize interference from non-sensitive frequency bands, combining wavelet packet transform, a “two-step” damage identification process based on sensitive frequency band fusion is proposed: the frequency bands near the natural frequency are initially picked out, and the displacement of these bands are integrated to damage localization. Next, based on the localization results, effective frequency bands are refined and their dynamic displacement curvature energy difference are fused to form the comprehensive index for damage identification. Finally, the engineering feasibility of the method is verified through numerical simulation of continuous girder bridge and the real bridge damage test of Japan Old ADA Bridge. The results show that under random and vehicle load, this method can effectively identify single or multiple location damage, and it demonstrates good identification capability under sparse measurement points and noisy environments, showing broad application prospects.

桥梁由于长期荷载、环境侵蚀和自然因素的作用，容易发生损坏和退化。作为结构健康监测的关键目标，损伤识别尤为重要。针对传统曲率方法计算复杂、精度不高、对传感器要求高的问题，提出了一种基于动态位移曲率能量差的方法。首先，通过欧拉-伯努利梁理论和频响函数分析，推导了不同频率范围内位移响应与振型之间的解析关系；结果表明，在固有频率附近的位移响应是识别损伤的有效方法。然后，为了简化计算和减少非敏感频段的干扰，结合小波包变换，提出了一种基于敏感频段融合的“两步”损伤识别过程：首先提取出固有频率附近的频段，并将这些频段的位移积分到损伤定位中。其次，基于局部化结果，对有效频段进行细化，并融合其动态位移曲率能量差，形成损伤识别的综合指标；最后，通过连续梁桥数值模拟和日本老ADA桥实桥损伤试验，验证了该方法的工程可行性。结果表明，在随机载荷和车辆载荷作用下，该方法能有效识别单个或多个位置损伤，且在测点稀疏和噪声环境下具有良好的识别能力，具有广阔的应用前景。

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引用次数: 0

Optimized acoustic computed tomography for monitoring urban road tunnel fire-induced ceiling temperatures using simulated annealing and SART 利用模拟退火和SART对城市道路隧道火灾天花板温度监测的优化声学计算机断层扫描

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113930

Zihe Gao, Xiaocui Wei, Huizhen Li, Yaqi Fan, Xinru Zhang

To address the risk of fire in tunnels, acoustic computed tomography (CT), is introduced for tunnel fire detection where the Simultaneous Algebraic Reconstruction Technique (SART) is used to reconstruct the ceiling temperature distribution in a tunnel with a heat release rate of 3.0 MW. The reconstruction of a selected area demonstrates that optimal results are achieved when the reconstructive grid is divided into cells with approximately equal length and width, densely distributed while avoiding overlap with effective acoustic paths. This optimal grid scheme is then applied to the entire ceiling area under the tunnel. Based on this, the ratio of acoustic transceiver spacing is further analyzed, which showed that the most economical spacing ratio is 4:1. By adopting the 4:1 strategy for the reconstruction of the entire tunnel ceiling area, the number of acoustic transceivers is reduced from 112 to 34, which greatly improves the economy and practicality of the program. Finally, two additional fire scenarios are investigated: one with dual symmetrical fire sources and another with a single biased fire source located off the tunnel centerline. The reconstruction under the dual-source scenario yields satisfactory results, further validating the effectiveness of the proposed approach. However, in the biased ignition case, near the fire source, a significant reduction in the number of effective acoustic paths is observed, which compromises the reconstruction accuracy. To address this, a Simulated Annealing (SA) algorithm is introduced to optimize the transceiver layout, and the results further confirm the robustness and adaptability of the proposed reconstruction strategy, providing a universal and high-precision optimization method for global monitoring of tunnel fires.

为了解决隧道火灾的风险，将声学计算机断层扫描（CT）引入隧道火灾探测，并利用同步代数重建技术（SART）重建热释放率为3.0 MW的隧道顶板温度分布。对选定区域的重建表明，将重建网格划分为长度和宽度大致相等的单元，密集分布，同时避免与有效声路径重叠，可获得最佳结果。然后将此优化网格方案应用于隧道下方的整个天花板区域。在此基础上，进一步分析了声收发器的间距比，得出最经济的间距比为4:1。通过对整个隧道顶棚区域采用4:1的重构策略，将声波收发器的数量从112台减少到34台，大大提高了方案的经济性和实用性。最后，研究了另外两种火灾情景：一种是双对称火源，另一种是位于隧道中心线以外的单一偏置火源。双源场景下的重建结果令人满意，进一步验证了所提方法的有效性。然而，在偏置点火情况下，在火源附近，观察到有效声路径的数量显著减少，这影响了重建的精度。为了解决这一问题，引入了模拟退火算法对收发器布局进行优化，结果进一步验证了所提重构策略的鲁棒性和适应性，为隧道火灾全局监测提供了一种通用的高精度优化方法。

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引用次数: 0

Track migration on 3D potential energy surface via magnetically perturbed rotation to enhance multi-stable energy harvesting in weak ocean excitations 利用磁摄动旋转在三维位能表面上进行航迹偏移，增强弱海洋激励下的多稳态能量收集

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113934

Yuanbin Mao, Shiyu Lu, Qingqing Liu, Ao Yin, Yuyang Zhang, Keyi Wang, Yu Zhang, Ruhan Guo, Ling Bu

Harvesting abundant wave energy for powering distributed marine sensors is the key to realizing Digital Ocean Strategy. Bi-stable energy harvesters are of superb low-frequency response, yet the stochastic ocean waves can hardly overcome the potential barrier, esp. the ultra-low excitations. Unlike previous works which physically or nonlinearly reduce the barrier on a specific two-dimensional potential energy function, this work devises a rotation-vibration coupled multi-stable energy harvester, which encompasses multiple potential energy functions forming a three-dimensional potential energy surface in the displacement-angle phase space. This potential energy surface offers alternative routes for energy track migration by varying the coupled rotation angle, which relieves the requisite of sufficiently high excitations. The energy track migration mechanism is theoretically explored, and the nonlinear dynamics associated with the potential energy surface are analyzed. In experimental verification, the device exhibits rich motion states and trajectories under different excitation conditions. Results show that under 0.4 g ultra-low excitation, the peak-to-peak voltage reaches 4.5 V, and the root-mean-square voltage reaches 1 V, demonstrating effective energy harvesting under weak excitations. A marine sensing prototype is realized by integrating the proposed multi-stable energy harvester with processing circuit and data acquisition unit. The prototype successfully collects and transmits temperature and position data in virtual marine environment, demonstrating practical applicability of the proposed device for self-powered marine sensing.

获取丰富的波浪能为分布式海洋传感器供电是实现数字海洋战略的关键。双稳态能量采集器具有极好的低频响应特性，但随机海浪难以克服其势垒，特别是超低激励。与以往的研究不同，本文设计了一种旋转-振动耦合的多稳态能量采集器，它包含了多个势能函数，在位移-角度相空间中形成了一个三维势能面。该势能面通过改变耦合旋转角度为能量轨迹迁移提供了替代途径，从而减轻了对足够高激励的要求。从理论上探讨了能量轨迹迁移机理，分析了与势能面相关的非线性动力学。在实验验证中，该装置在不同激励条件下表现出丰富的运动状态和运动轨迹。结果表明，在0.4 g的超低激励下，峰间电压达到4.5 V，均方根电压达到1 V，在弱激励下实现了有效的能量收集。将所提出的多稳态能量采集器与处理电路和数据采集单元集成，实现了海洋传感样机。样机成功地在虚拟海洋环境中采集和传输温度和位置数据，证明了该装置在自供电海洋传感中的实际适用性。

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引用次数: 0

Nondestructive subwavelength metablocks for manipulation of shear horizontal waves 用于剪切水平波操作的非破坏性亚波长元块

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-28 DOI: 10.1016/j.ymssp.2026.113936

Mingtao Fu, Hongchen Miao, Guozheng Kang

Metamaterial-aided ultrasonic nondestructive testing (NDT) has emerged as a promising field for overcoming limitations in conventional defect detection. However, the practical implementation encounters challenges because traditional metamaterial design typically requires breaking structural continuity, and the 2D/3D periodic distribution of metamaterials leads to a significant increase in complexity. This study presents an innovative metablock design strategy characterized by nondestructive properties, one-dimensional periodicity, and broadband performance (20 kHz bandwidth). Focusing on the fundamental shear horizontal (SH₀) wave, a preferred mode in NDT due to its nondispersive propagation characteristic, a subwavelength rectangular metablock capable of effective wave manipulation through antiresonance mechanisms is developed. The proposed metablock can be easily manufactured and attached to the host structure without compromising the structure’s integrity. A theoretical model is developed to describe the mechanism of wave isolation and simultaneously design the metablock’s dimensions. Practical demonstrations include one-dimensional isolators blocking full-angle incident SH₀ waves and waveguides guiding SH₀ wave propagation, which are validated by simulations and experiments. Furthermore, a metablock-aided inspection system is developed to demonstrate enhanced detection capabilities for oblique cracks (oriented between 0° and 90°), most of which fall outside the capability of conventional Pulse-echo ultrasonic testing. Simulation and experimental results confirm significant improvements of the proposed system in crack identification accuracy and extended inspection coverage with a wide frequency range, highlighting the metablock’s promising applications in SH₀ wave-based NDT.

超材料辅助超声无损检测（NDT）已成为克服传统缺陷检测局限性的一个有前途的领域。然而，由于传统的超材料设计通常需要打破结构连续性，并且超材料的2D/3D周期性分布导致复杂性显着增加，因此在实际实施中遇到了挑战。本研究提出了一种创新的元块设计策略，其特点是具有无损特性、一维周期性和宽带性能（20 kHz带宽）。针对基波剪切水平波（SH0）这一非色散传播特性在无损检测中的首选模式，开发了一种亚波长矩形元块，该元块能够通过反共振机制有效地操纵波。所提出的元块可以很容易地制造并附着在宿主结构上，而不会损害结构的完整性。建立了一个理论模型来描述隔波机理，同时设计元块的尺寸。实际演示包括阻挡全角度入射SH0波的一维隔离器和引导SH0波传播的波导，并通过仿真和实验进行了验证。此外，研究人员还开发了一种元块辅助检测系统，以增强斜裂纹（取向在0°到90°之间）的检测能力，而大多数斜裂纹都超出了常规脉冲回波超声检测的能力。仿真和实验结果证实了该系统在裂纹识别精度和宽频率范围内的检测范围方面的显着提高，突出了元块在基于SH0波的无损检测中的应用前景。

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引用次数: 0

Structural damage identification based on pattern-coupled sparse Bayesian learning 基于模式耦合稀疏贝叶斯学习的结构损伤识别

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-27 DOI: 10.1016/j.ymssp.2026.113893

Jiasen Lin , Rongrong Hou , Yuequan Bao

Structural damage identification inevitably involves uncertainties, necessitating their explicit consideration to enhance the reliability and precision of detection frameworks. As a prominent sparse recovery technique, sparse Bayesian learning (SBL) has demonstrated effectiveness in damage identification by leveraging structural sparsity through automatic relevance determination (ARD) priors. However, conventional SBL implementations adopt an oversimplified probabilistic model that assumes mutual independence among damage parameters, thereby failing to account for inherent spatial correlations between adjacent structural elements. This study proposes a novel pattern-coupled SBL methodology that incorporates coupled Gaussian priors to simultaneously characterize and autonomously learn both sparsity patterns and parameter correlations. This dual-learning mechanism enables enhanced precision in quantifying damage severity through correlation-aware parameter estimation, and improved robustness against measurement noise and modeling errors. Furthermore, the proposed framework extends conventional sparse recovery capabilities by effectively resolving both distributed and block-sparse damage configurations—a crucial feature where traditional SBL approaches exhibit suboptimal performance. Numerical studies on a cable-stayed bridge model and experimental investigations of a space frame validate the method’s effectiveness in accurately identifying and quantifying single and multiple damage scenarios. Compared with the SBL method, the identification accuracy and robustness of the proposed method are significantly improved, especially for structural damage with block-sparse characteristics.

结构损伤识别不可避免地涉及不确定性，为了提高检测框架的可靠性和精度，需要明确地考虑不确定性。作为一种突出的稀疏恢复技术，稀疏贝叶斯学习（SBL）通过自动关联确定（ARD）先验来利用结构稀疏性，在损伤识别方面已经证明了其有效性。然而，传统的SBL实现采用了一种过于简化的概率模型，该模型假定损伤参数之间相互独立，因此未能考虑相邻结构元素之间固有的空间相关性。本研究提出了一种新的模式耦合SBL方法，该方法结合了耦合高斯先验，同时表征和自主学习稀疏模式和参数相关性。这种双重学习机制通过相关感知参数估计提高了量化损伤严重程度的精度，并提高了对测量噪声和建模误差的鲁棒性。此外，提出的框架通过有效地解决分布式和块稀疏损坏配置（传统SBL方法表现出次优性能的关键特征）扩展了传统的稀疏恢复能力。斜拉桥模型的数值研究和空间框架的实验研究验证了该方法在准确识别和量化单一和多重损伤情景方面的有效性。与SBL方法相比，该方法的识别精度和鲁棒性显著提高，特别是对于具有块稀疏特征的结构损伤。

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引用次数: 0

Sonic black hole coupled with membrane-type acoustic metamaterial for broadband and low-frequency sound absorption 超声黑洞与膜型声学超材料耦合用于宽带和低频吸声

IF 8.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2026-01-27 DOI: 10.1016/j.ymssp.2026.113932

Wei-Qin Wu , Yong-Bin Zhang , Liu-Xian Zhao , Ting-Gui Chen , Yi-Feng Wang

Simultaneous low-frequency and broadband absorption is still difficult to achieve in compact acoustic metamaterials, as most existing designs address only one aspect. To address this limitation, a hybrid acoustic metastructure combining a sonic black hole with a membrane-type acoustic metamaterial is proposed to realize efficient broadband absorption at low frequencies within a compact configuration. A transfer matrix model, validated by finite element simulations, confirms that the sonic black hole provides broadband dissipation by guiding and attenuating acoustic energy, while the membrane-type acoustic metamaterial introduces tunable low-frequency resonances. Parametric studies further reveal the critical influence of the coupling cavity and back cavity dimensions in shaping the absorption peaks. Comparative analyses with conventional sonic black hole-based designs demonstrate that the proposed acoustic metastructure achieves superior low-frequency control and compactness. Finally, impedance tube experiments corroborate the numerical predictions, underscoring the strong potential of the acoustic metastructure for practical broadband low-frequency noise control applications.

在紧凑的声学超材料中，同时实现低频和宽带吸收仍然很困难，因为大多数现有的设计只涉及一个方面。为了解决这一限制，提出了一种结合声波黑洞和膜型声学超材料的混合声学元结构，以在紧凑的结构中实现低频的高效宽带吸收。通过有限元模拟验证的传递矩阵模型证实，声波黑洞通过引导和衰减声能提供宽带耗散，而膜型声学超材料则引入可调谐的低频共振。参数研究进一步揭示了耦合腔和后腔尺寸对吸收峰形成的关键影响。与传统声学黑洞设计的对比分析表明，所提出的声学元结构具有优越的低频控制和紧凑性。最后，阻抗管实验证实了数值预测，强调了声学元结构在实际宽带低频噪声控制应用中的强大潜力。

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