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On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-09 Andrew Bunce, Daniel S. Brennan, Alan Ferguson, Connor O'Higgins, Su Taylor, Elizabeth J Cross, Keith Worden, James Brownjohn, David Hester
Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to
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Transmissibility-based operational modal analysis: A unified scheme and uncertainty quantification Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-09 Jie Kang, Jiabao Sun, Jie Luo, Xiaoteng Liu
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High-speed bearing diagnostics: Observations from the Surveillance 8 Safran contest data Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-08 Wade A. Smith, Pietro Borghesani, Robert B. Randall, Jérôme Antoni, Mohammed El Badaoui, Zhongxiao Peng
It is usually assumed that faulty bearings produce second-order cyclostationary (CS2) signals, and thus the natural process for their diagnostic analysis involves first the removal of first-order cyclostationary (CS1) components, such as from gears, followed by amplitude demodulation of an ‘informative’ frequency band, and subsequent envelope analysis, in which the spectrum of the (squared) envelope
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Time series diffusion method: A denoising diffusion probabilistic model for vibration signal generation Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-08 Haiming Yi, Lei Hou, Yuhong Jin, Nasser A. Saeed, Ali Kandil, Hao Duan
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Identification of linear flat outputs using neural networks—Examples of two-degree-of-freedom underactuated mechanical systems Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-08 Shangjie Frank Ma, Anni Zhao, Jian-Qiao Sun
This paper proposes a neural networks-based approach of finding flat output of linearized underactuated mechanical systems (UMS). Given that differential flatness and controllability are equivalent for linear systems, the problem is equivalent to finding the Brunovsky canonical form of linearized UMSs. We use a two degree-of-freedom (2DOF) system to illustrate the theoretical development. The proposed
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Dynamic error prediction and link strain feedback control for a novel heavy load multi-DOF envelope forming machine Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-07 Fangyan Zheng, Xinghui Han, Lin Hua, Wuhao Zhuang, Bo Huang
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Coupling effect of machine tool dynamic characteristics and cutting conditions on the cutting process vibration and high-speed micro-planing surface mid-frequency waviness Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-06 Lizi Qi, Min Zhu, Qiang Gao, Yabo Zhang, Guoyu Fu, Qi Cui, Siyu Gao, Wenyuan Wei, Lexiang Wang, Lihua Lu
In addition to the dynamic characteristics of the machine tool, cutting conditions significantly influences the vibration of the cutting system and mid-frequency waviness of the workpiece surface in ultra-precision machining (UPM). In this work, the effects of cutting conditions on machining vibration and surface waviness were investigated by high-speed micro-planning experiments. The origin of vibration
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Basis pursuit set selection for nonlinear underconstrained problems: An application to damage characterization Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-06 Dionisio Bernal, Martin D. Ulriksen
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An experimental approach to multi-input multi-output nonlinear active vibration control of a clamped sandwich beam Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-06 Celia Hameury, Giovanni Ferrari, Giulio Franchini, Marco Amabili
Large amplitude vibrations are often associated with geometric nonlinearity. These nonlinear systems are usually controlled using linear controllers, such as positive position feedback (PPF). Nonlinear control has also often been limited to single-input single-output (SISO) architectures. The present study develops a nonlinear PPF controller implemented with both a SISO and a multiple-input multiple-output
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Iterative improvement in tacholess speed estimation using instantaneous error estimation for machine condition monitoring in variable speed Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-06 Dikang Peng, Wade A. Smith, Robert B. Randall, Ke Feng, Zhongxiao Peng, Wei Teng, Yibing Liu
Knowing the instantaneous angular speed (IAS) is crucial for monitoring the condition of variable speed rotating machinery. Thanks to advantages such as cost-saving, simplicity, and reduced installation difficulties, tacholess speed estimation (TSE) methods, based on the vibration signal itself, have attracted increasing attention in recent years. The major problem limiting the use of TSE methods in
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M-band wavelet network for machine anomaly detection from a frequency perspective Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-04 Zuogang Shang, Zhibin Zhao, Ruqiang Yan, Xuefeng Chen
The autoencoder (AE) is widely utilized in deep anomaly detection, but it lacks explainability due to the complexity of nonlinear mapping. One approach to address this issue is incorporating wavelet theory, which shares similarities in decomposition and reconstruction procedures. However, the perfect reconstruction property of wavelet theory conflicts with AE-based anomaly detection. To tackle this
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Robustness analysis and experimental validation of a deep neural network for acoustic source imaging Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-04 Qing Li, Elias J.G. Arcondoulis, Sheng Wei, Pengwei Xu, Yu Liu
Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods
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Weight extracting transform for instantaneous frequency estimation and signal reconstruction Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-04 Cuiwentong Xu, Yuhe Liao
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A Gaussian-process assisted model-form error estimation in multiple-degrees-of-freedom systems Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-04 Sahil Kashyap, Timothy J. Rogers, Rajdip Nayek
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A patterned vibrotactile method using envelope modulation with high resolution and low perceptual frequency Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-04 Hangyu Li, Zewei Hou, Jijing Huang, Li Zhou, Yongmao Pei
The virtual haptics is a crucial aspect of immersive virtual reality, extending the traditional experiences of sight and hearing. The vibration can provide direct normal vibration and friction reduction, making it a promising method to realize virtual haptics. However, the optimum perceptual threshold of skin for vibration is 100 Hz to 500 Hz, resulting in a too low vibrotactile resolution. The conflict
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Machine vision and novel attention mechanism TCN for enhanced prediction of future deposition height in directed energy deposition Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-03 Miao Yu, Lida Zhu, Jinsheng Ning, Zhichao Yang, Zongze Jiang, Lu Xu, Yiqi Wang, Guiru Meng, Yiming Huang
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Feedback control system for vibration construction of fresh concrete Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-03 Jiajie Li, Zhenghong Tian, Yuanshan Ma, Lujia Li, Weihao Shen, Jiaxing Zhao
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Constructing nonlinear data-driven models from pitching wing experiments using multisine excitation signals Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-03 M.F. Siddiqui, P.Z. Csurcsia, T. De Troyer, M.C. Runacres
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A mechanics-informed neural network method for structural modal identification Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-03 Yuequan Bao, Dawei Liu, Hui Li
Modal identification is one of the core topics within the realm of structural health monitoring (SHM). In this study, we summarize four modal mechanical properties and propose a mechanics-informed neural network (MINN) method for structural modal identification. The proposed MINN method incorporates the sparsity of the data in the time–frequency domain and cross-correlation minimization in the time
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Smooth least absolute deviation estimators for outlier-proof identification Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-03 Janusz Kozłowski, Zdzisław Kowalczuk
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Theoretical and experimental study of a stable state adjustable nonlinear energy sink Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-02 You-Cheng Zeng, Hu Ding, Jin-Chen Ji, Li-Qun Chen
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An improved LuGre friction model and its parameter identification of structural interface in thermal environment Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-02 Tichang Jia, Jie Liu, Yunzhao Wang, Chaofeng Li, Haoyan Zhang
In this paper, an improved LuGre model was established based on the micro-convex assumption, Hertz contact theory, and thermal conditions. The displacement-tangential force and velocity-tangential force hysteresis curves under different temperature conditions were obtained by the dry friction testing experiment. Further, this paper constructed an objective function for the proposed friction model,
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Stability prediction method of time-varying real-time hybrid testing system on vehicle-bridge coupled system Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-02 Hao Liu, Zhenyun Tang, Ryuta Enokida
In recent years, real-time hybrid testing (RTHT) has been applied for the dynamic testing of high-speed trains running on bridges. A guarantee of stability for the RTHT system is essential to achieve a safe and reliable result. However, the inherent time-varying characteristics of the vehicle-bridge coupled system pose challenges to RTHT stability prediction. This study aims to develop a stability
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A novel mirror-assisted method for full-field vibration measurement of a hollow cylinder using a three-dimensional continuously scanning laser Doppler vibrometer system Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-02 K. Yuan, W.D. Zhu
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Zero group velocity feature in CFRP-Nomex honeycomb structure and its use for debonding detection Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-01 Ye Yuan, Bin Liu, Zhengxiao Sha, Zhiguo Zhang, Zheng Wang
Carbon fiber reinforced plastic (CFRP) − Nomex adhesive honeycomb structures are widely used in aerospace due to their excellent properties. However, debonding defects pose a significant challenge to structural safety due to their hidden nature and high risk. In this work, to address the debonding detection in the CFRP-Nomex honeycomb structure, a method based on the zero group velocity (ZGV) feature
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Multi-agent reinforcement learning method for cutting parameters optimization based on simulation and experiment dual drive environment Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-30 Weiye Li, Caihua Hao, Songping He, Chaochao Qiu, Hongqi Liu, Yanyan Xu, Bin Li, Xin Tan, Fangyu Peng
Improving production efficiency while ensuring product surface quality is a constant focus of manufacturers. Cutting parameter optimization is an important technique for ensuring high-efficiency and high-quality production. In this paper, a novel method for cutting parameter optimization that integrates multi-agent reinforcement learning with a dual-drive virtual machining environment is proposed.
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Electroelastic wave dispersion in the rotary piezoelectric NEMS sensors/actuators via nonlocal strain gradient theory Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-30 Yuan Guo, Allam Maalla, Mostafa Habibi, Zohre moradi
This article introduces a computational means for investigating the electroelastic nonlinear wave dispersion traits of the nano-dimension sandwich pipe, which is composed of a core formed of a bi-directional functionally graded (Bi-FG) material, together with a piezoelectric sensor/actuator. A combination of Hamilton’s principle, first-order shear deformation, along with Von-Karman nonlinearity, is
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Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-30 Jan Grashorn, Matteo Broggi, Ludovic Chamoin, Michael Beer
In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using
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A novel mode coupling mechanism for predicting low-frequency chatter in robotic milling by providing a vibration feedback perspective Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-30 Jiawei Wu, Xiaowei Tang, Fangyu Peng, Rong Yan, Shihao Xin
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A multi-band elastic metamaterial for low-frequency multi-polarization vibration absorption Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-29 Shiteng Rui, Weiquan Zhang, Rihuan Yu, Xingzhong Wang, Fuyin Ma
The vibration of engineering structures in actual practice occurs across numerous frequency ranges and includes diverse polarization modes such as bending, torsion, and expansion. Nevertheless, most reported elastic metamaterials are designed for a single frequency range or a single elastic wave mode, thereby making it challenging to simultaneously suppress the propagation of vibrational energy across
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Mesh stiffness calculation of defective gear system under lubrication with automated assessment of surface defects using convolutional neural networks Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-29 Siyu Wang, Penghao Duan
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Twist compensated, high accuracy and dynamic fiber optic shape sensing based on phase demodulation in optical frequency domain reflectometry Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Sheng Li, Qingrui Li, Zhenyang Ding, Kun Liu, Huafang Wang, Peidong Hua, Haohan Guo, Teng Zhang, Ji Liu, Junfeng Jiang, Tiegen Liu
We present a twist compensated, high accuracy and dynamic fiber optic shape sensing based on phase demodulation in Optical Frequency Domain Reflectometry (OFDR) by using multiple single core fiber based sensor (MFS). A dynamic strain sensing is realized by tracking the optical phase in OFDR and combining with the phase de-hopping filtering algorithm, and the sensing spatial resolution reaches 45 μm
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A Bayesian network development methodology for fault analysis; case study of the automotive aftertreatment system Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Morteza Soleimani, Sepeedeh Shahbeigi, Mohammad Nasr Esfahani
This paper proposes a structured methodology for generating a Bayesian network (BN) structure for an engineered system and investigates the impact of integrating engineering analysis with a data-driven methodology for fault analysis. The approach differs from the state of the art by using different initial information to build the BN structure. This method identifies the cause-and-effect relationships
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Frequency response function-based closed-form expression for multi-damage quantification and its application on shear buildings Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Saranika Das, Koushik Roy
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Optimal weight impulse extraction: New impulse extraction methodology for incipient gearbox condition monitoring Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Xiaofei Liu, Naipeng Li, Yaguo Lei, Dong Wang, Qubing Ren, Jinze Jiang, Yuan Wang
Gear faults in a transmission system generally cause impulse components in vibration signals, which is a crucial symbol for gearbox fault diagnosis. However, their related signals are often interfered or even submerged by the noisy meshing components (NMC) of gearboxes in degradation, which introduces challenges for incipient fault detection and condition monitoring. Commonly employed deconvolution-based
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Floating offshore wind turbine mooring line sections health status nowcasting: From supervised shallow to weakly supervised deep learning Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Andrea Coraddu, Luca Oneto, Jake Walker, Katarzyna Patryniak, Arran Prothero, Maurizio Collu
The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service
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Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-27 Bowen Zhang, Xianli Liu, Caixu Yue, Steven Y. Liang, Lihui Wang
Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learning network with adaptive gradients (HFBEAML) is proposed
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Enhanced selective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-26 Xiaolong Li, Chihua Lu, Wan Chen, Zhien Liu, Can Cheng, Yongliang Wang, Songze Du
The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a
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An MFC-based friction damper with adjustable normal force: conception, modelling, and experiment Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-26 Y.G. Wu, J.B. Chen, Y. Fan, L. Li, Z. Jiang
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A High-Performance piezoelectric micropump designed for precision delivery Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-25 Meng Wang, Luntao Dong, Runyu Liu, Conghui Wang, Xiaodong Sun, Xinbo Li, Guojun Liu, Zhigang Yang
A piezoelectric micropump (PE pump) was proposed featuring a multi-plate cantilever valve (MPCV) and a ramp channel (RC) to deliver high performance in a compact design. Both the MPCV and RC underwent thorough theoretical, simulation-based, and experimental evaluations. A specialized driver plate was then developed to precisely control the PE pump. Key parameters of the PE pump were optimized based
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Wide quasi-zero stiffness region isolator with decoupled high static and low dynamic stiffness Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-25 Wenjun Shi, Weiqun Liu, Chunrong Hua, Hongkun Li, Qiao Zhu, Dawei Dong, Yanping Yuan
Quasi-zero stiffness (QZS) isolators can achieve the two goals of relatively high static and low dynamic stiffness (HSLDS). However, the static and dynamic stiffness of most QZS isolators remains coupled, causing conflicts in optimizing these dual objectives, especially in the case of large displacement excitations and heavy loads, leading to limited performance. To overcome these limitations, this
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Experimental comparison of three automatic operational modal analysis algorithms on suspension and floating bridges Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-25 Anno Christian Dederichs, Gunnstein T. Frøseth, Ole Øiseth
Automatic operational modal analysis is necessary for long-term monitoring of structures when using modal information. Many algorithms have been proposed to accomplish this task; two examples are the fully automatic algorithm by Reynders et al. in 2012 and the semi-automatic algorithm by Kvåle and Øiseth in 2020; however, few in-depth direct comparisons exist. This work compares the two algorithms
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A CNN-BiLSTM-Attention approach for EHA degradation prediction based on time-series generative adversarial network Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-25 Zhonghai Ma, Yiwen Sun, Hui Ji, Suolan Li, Songlin Nie, Fanglong Yin
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Damping prediction of highly dissipative meta-structures through a wave finite element methodology Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-25 Dongze Cui, Noureddine Atalla, Mohamed Ichchou, Abdel-Malek Zine
Aiming at accurately predicting the global Damping Loss Factor (DLF) for Highly Dissipative Structures (HDS), the current study uses the Wave Finite Element (WFE) methodology. It starts by deriving the forced responses of a Unit Cell (UC) representative of the periodic meta-structure. Then it computes the DLF of the wave via the power balance. The Bloch expansion is employed. The response to a point
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Corrigendum to “Damage localization using acoustic emission sensors via convolutional neural network and continuous wavelet transform” [Mech. Syst. Signal Process. 204 (2023) 110831] Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-24 Van Vy, Yunwoo Lee, JinYeong Bak, Solmoi Park, Seunghee Park, Hyungchul Yoon
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Full-field displacement measurement of long-span bridges using one camera and robust self-adaptive complex pyramid Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Yuchao Wang, Weihua Hu, Jun Teng, Yong Xia
Full-field motion with a high spatial resolution can reflect the health state of long-span bridges. Traditional structural health monitoring (SHM) systems measure the structural displacement at sparse points only. Despite the development of various methods for obtaining high-resolution responses, they fail to estimate the multi-scale motions of real long-span bridges. A novel full-field motion estimation
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The assignment of zero sound pressure frequencies using measured sound pressure receptances and structural receptances Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Yingsha Shi, Sheng Li
In structural receptances, the zeros (antiresonances) define those frequencies at which vibrations disappear. In this paper, the zero sound pressure frequency is defined as the frequency at which the sound pressure is zero at certain locations. A method for the assignment of zero sound pressure frequencies using measured sound pressure receptances and structural receptances is proposed through two
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Physics-based prognostics of rolling-element bearings: The equivalent damaged volume algorithm Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Alberto Gabrielli, Mattia Battarra, Emiliano Mucchi, Giorgio Dalpiaz
This paper introduces a novel parameter related to bearing degradation, namely the Equivalent Damaged Volume (EDV). An algorithm capable of extracting EDV values from experimental data is detailed. To this end, the proposed technique relies on the comparison between experimental and numerical signals. The former are the result of an extensive campaign of run-to-failure tests performed on a dedicated
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rLSTM-AE for dimension reduction and its application to active learning-based dynamic reliability analysis Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Yu Zhang, You Dong, Michael Beer
A novel method termed rLSTM-AE is developed for the low-dimensional latent space identification of the stochastic dynamic systems with more than 1000 input random variables and the active learning-based dynamic reliability analysis. First, the long short-term memory network considers both the time-variant stochastic excitation and the time-invariant random variables is developed (rLSTM), which adopts
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Tracking superharmonic resonances for nonlinear vibration of conservative and hysteretic single degree of freedom systems Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Justin H. Porter, Matthew R.W. Brake
Many modern engineering structures exhibit nonlinear vibration. Characterizing such vibrations efficiently is critical to optimizing designs for reliability and performance. For linear systems, steady-state vibration occurs only at the forcing frequencies. However, nonlinearities (e.g., contact, friction, large deformation, etc.) can result in nonlinear vibration behavior including superharmonics —
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Estimating structural motions in extreme environmental conditions——A dynamic correlation filter based computer vision approach Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-23 Enjian Cai, Yi Zhang, Xinzheng Lu, Xiaodong Ji, Xiang Gao, Jiale Hou, Ji Shi, Wei Guo
Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM). However, these methods are not standard practices yet, since their accuracy and robustness may be influenced by extreme environmental conditions. To this end, this paper proposed a method, named dynamic regularized total variation correlation filter (DTVCF). In DTVCF, an effective optimization problem
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Experimental nonlinear model of a set of connecting elements in view of nonlinear modal coupling Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-22 Jacopo Brunetti, Walter D’Ambrogio, Annalisa Fregolent, Francesco Latini
The development process of mechanical systems involves the evaluation of its modes of vibrations in the frequency range of interest. In general, a linear modal analysis is sufficient to determine whether the system can operate in dynamic conditions. However, in some cases the assembly is composed of many subsystems connected through nonlinear connections which make the response depend on the amplitude
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Reliable arrival time picking of acoustic emission using ensemble machine learning models Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-19 Xiao Wang, Qingrui Yue, Xiaogang Liu
This study presents an innovative method for accurately picking the first-wave arrival time in acoustic emission (AE) localization, particularly effective in environments with low or variable signal-to-noise ratios (SNR). Utilizing an ensemble learning model, it synergizes multiple automatic arrival time estimation algorithms to enhance both consistency and robustness. The model, rooted in decision
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Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-19 Hao Ding, Zili Zhang, Jinting Wang, Jian Zhang, Okyay Altay
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Lamb waves-based PCF-DMA: An anti-interference synchronous independent data transmission scheme for multiple cross-space users Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-18 Yunfei Xu, Haoming Xiang, Xuegang Li, Hezhen Yu, Shaohua Chen, Wenbin Huang, Xiaoxi Ding
Due to the high cost and safety risk brought about by wire penetration within the aerospace and underwater structure, reliable wireless cross-space multiple access transmission techniques become highly necessary. Lamb waves data transmission is recognized as a feasible solution, since it utilizes the solid structures as the transmission medium without being affected by electromagnetic radiation. However
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Machine learning-based optimal design of an acoustic black hole metaplate for enhanced bandgap and load-bearing capacity Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-18 Sihao Han, Nanfang Ma, Qiang Han, Chunlei Li
This paper introduces a novel machine learning-based optimization strategy for multi-functional acoustic black hole (ABH) metaplates. The primary objective is to achieve a multi-functional metaplate with excellent performance in elastic wave attenuation and load-bearing capacity simultaneously. The paper begins by describing the design of nanocomposite ABH metaplates, presenting a new pathway to realize
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Physics-informed neural networks for acoustic boundary admittance estimation Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-18 Johannes D. Schmid, Philipp Bauerschmidt, Caglar Gurbuz, Martin Eser, Steffen Marburg
Acoustic simulations often face significant uncertainties due to limited knowledge of acoustic boundary conditions. While measuring the boundary admittance is challenging in practical applications, numerical inverse methods can be used to characterize the boundary conditions based on sound pressure data. However, conventional inverse methods require a validated forward model and can become impractical
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A guide to numerical dispersion curve calculations: Explanation, interpretation and basic Matlab code Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-18 Vanessa Cool, Elke Deckers, Lucas Van Belle, Claus Claeys
Dispersion diagrams play a crucial role in examining, analyzing and designing wave propagation in periodic structures. Despite their ubiquity and current research interest, introductory papers and reference scripting tailored to novel researchers in the field are lacking. This paper aims to address this gap, by presenting a comprehensive educational resource for researchers starting in the field of
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Constitutive model of metal rubber based on modified Iwan model under quasi-static compression and random vibration Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-17 Hang Yang, Xiangyu Chen, Chunwang He, Qiwen Zeng, Mingyong Wu, Gang Chen
Metal rubber has been widely applied in the fields of structural vibration reduction and impact protection, due to its excellent mechanical properties. However, an accurate constitutive model of metal rubber to characterize its complex nonlinear mechanical behavior is still lacking. In this paper, a new constitutive model of metal rubber based on the Iwan model (parallel spring-slider model) is proposed
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Semi-analytical modeling of thermo-metallurgical-induced wave propagation for titanium alloy parts in laser powder bed fusion Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-17 Zhi-Jian Li, Hong-Liang Dai, Yuan Yao, Yu-Song Li, Peng Xiao, Wei-Feng Luo
Thermal-induced wave propagation frequently occurs during the laser powder bed fusion (LPBF) process due to the laser-material interaction. However, the effect of thermal variation and the resulting metallurgical phase transition on the wave propagation characteristics remains unclear. This paper presents a semi-analytical modeling of thermo-metallurgical-induced wave propagation in the LPBF of titanium