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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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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
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Domain distribution variation learning via adversarial adaption for helicopter transmission system fault diagnosis Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-17 Kuangchi Sun, Aijun Yin, Shiao Lu
Deep Learning-based fault diagnosis has aroused widespread attention in machine fault diagnosis. Helicopter is an important transport for its special purpose. How to ensure its normal operation is a challenging task. Nevertheless, the existing research mainly focuses on single bearing or gear of gearbox, while there are few reports about intelligent fault diagnosis of bearing and shaft in helicopter
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A novel weighted sparsity index based on multichannel fused graph spectra for machine health monitoring Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-17 Kaifan Zhang, Jing Yuan, Huiming Jiang, Qian Zhao
Constructing validated health indices (HIs) is main strategy for machine health monitoring. Multichannel signals always contain richer condition information of machines than single channel signals, resulting in promising HIs constructed from multichannel signals. To fully utilize multichannel signals to construct HIs, adopting appropriate multichannel fusion strategies and extracting useful feature
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ADSOC: A novel automatic and deterministic shaft orbit classification framework for large rotating machinery Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-17 Cheng Hao Jin, Sheng Guo
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Higher-order parametrized correction based contact performance forecasting model for spiral bevel gears Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 Kaibin Rong, Biyun Song, Jianxing Wang, Xu Qiu, Guan Zhang, Shifeng Rong, Jinyuan Tang, Han Ding
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Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 Jianhua Zhong, Cong Lin, Yang Gao, Jianfeng Zhong, Shuncong Zhong
The paper proposes an unsupervised deep convolutional dynamic joint distribution domain adaptive network model for the problem of bearing fault diagnosis under variable conditions, which involves missing labeling of target domain data and large differences in the distribution of source and target domain data. The model consists of the following steps: (1) converting the original vibration signal of
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A novel phase-based video motion magnification method for non-contact measurement of micro-amplitude vibration Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 Yuanzhao Yang, Qi Jiang
Vibration analysis is crucial for structural health monitoring and fault diagnosis. Conventional contact sensors present limitations, prompting the adoption of non-contact methods such as laser Doppler vibration measurements and computer vision-based techniques. Among these, phase-based video motion magnification has gained prominence for its high resolution and ability to capture comprehensive vibration
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Phase resonance testing of highly flexible structures: Measurement of conservative nonlinear modes and nonlinear damping identification Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 Marielle Debeurre, Simon Benacchio, Aurélien Grolet, Clément Grenat, Christophe Giraud-Audine, Olivier Thomas
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Inverse surrogate model for deterministic structural model updating based on random forest regression Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 S. Kamali, S. Mariani, M.A. Hadianfard, A. Marzani
This article presents a novel method for deterministic finite element model updating that is based on an “inverse surrogate model”. The latter is a regression model that uses structural responses as independent variables and structural properties as dependent ones. Such regressor is trained on structural responses using a finite element model having in input the structural properties to be updated
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Finite word-length optimal simulation for high-dimensional dynamical systems: Examples of tensegrity structures Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 Yuling Shen, Muhao Chen, Robert E. Skelton
This paper presents a model reduction technique to determine the optimal simulation model for high-dimensional systems within the confines of finite word-length computing. Such an optimal model is characterized by having minimal output error covariances in computational simulations when compared with the outputs of the physics model in reality. The round-off noise models for both floating- and fixed-point
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The spatial Fourier summation of corrugated beams and their band gap formation Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-16 P.B. Lamas, R. Nicoletti
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Cutting vibration characteristics and mechanisms in the end milling of superalloy honeycomb core with ice fixation clamping Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-15 Shaowei Jiang, Haibo Liu, Yueshuai Zuo, Daomian Sun, Yuebing Yang, Di Zhao, Kuo Liu, Yongqing Wang
Superalloy honeycomb cores possess excellent properties such as high strength, high stiffness, heat insulation, and vibration isolation, and are widely used in aerospace, and defense industries. Honeycomb materials, due to their thin-walled porous structure, anisotropy, and weak in-plane rigidity, exhibit complex and significant cutting vibration under the intermittent impact of the tool, which differs
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DP2Net: A discontinuous physical property-constrained single-source domain generalization network for tool wear state recognition Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-15 Xuwei Lai, Kai Zhang, Qing Zheng, Minghang Zhao, Guofu Ding, Baoping Tang, Zisheng Li
Cross-conditions tool wear monitoring has a wide application prospect in manufacturing. However, the data distribution discrepancies caused by the inconsistency of process elements restrict the generalization of models under cross-conditions or even similar conditions. The existing methods based on diversity enhancement make it difficult to effectively establish the correlation between the source domain
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Physics-Informed deep Autoencoder for fault detection in New-Design systems Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-15 Chenyang Lai, Piero Baraldi, Enrico Zio
The industrial application of data-driven methods for fault detection of new-design systems is limited by the inevitable scarcity of real data. Physics-Informed Neural Networks (PINNs) can mitigate this problem by integrating data and physical knowledge. In this work, we develop a novel fault detection method that combines physics-based simulations for data generation with a Physics-Informed Deep Autoencoder
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Machine Learning approaches to damage detection in composite structures combining experimental and simulation domains Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-15 André Tavares, Emilio Di Lorenzo, Bram Cornelis, Bart Peeters, Wim Desmet, Konstantinos Gryllias
Composite materials are widely used across major industries such as the automotive, aerospace and wind power, due to their excellent mechanical properties. A strong effort is thus put into developing innovative damage detection methodologies, for which Non-Destructive Testing (NDT) techniques can play a vital role as advanced measurement methods. One such technique is Laser Doppler Vibrometry which
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A relationship between fatigue damage estimation under multi-axis and single-axis random vibration Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-15 Enrico Proner, Emiliano Mucchi, Roberto Tovo
Random vibration testing is traditionally performed by means of single-axis testing. However, real operational environment are in general characterized by multi-axis vibration. As a consequence, single-axis testing is incapable of reproducing the actual damaging process of a component in the laboratory. In this work the damage inflicted to a cantilever beam by multi-axis and sequential single-axis
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An improved near-field weighted subspace fitting algorithm based on niche particle swarm optimization for ultrasonic guided wave multi-damage localization Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-14 Xin Fang, Guijie Liu, Honghui Wang, Weilei Mu, Yingchun Xie, Xiaojie Tian, Gongbo Li, Guanghao Li
The ultrasonic guided wave-based method for multi-damage localization has been widely proposed. However, the precision of this method is directly correlated with both the quantity of sensors employed and the intricacy of the implementation process. This relationship poses a challenge in striking a balance between the accuracy and efficiency. To improve the computational efficiency under the premise
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Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-13 Qiang Li, Xiuli Du, Pinghe Ni, Qiang Han, Kun Xu, Yulei Bai
Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked
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On the strain-sensing capabilities of a novel all-solid-state sodium-based-electrolyte battery under vibration loads Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-13 Bruno Guilherme Christoff, Denys Marques, João Paulo Carmo, Maria Helena Braga, Volnei Tita
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Pipe wall thickness estimation by frequency–wavenumber analysis of circumferential guided waves Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-13 Magnus Wangensteen, Tonni Franke Johansen, Ali Fatemi, Erlend Magnus Viggen
Ultrasonic guided waves in pipes propagating in the circumferential direction carry information about the thickness of the pipe wall. This study proposes a method for estimating the pipe wall thickness based on measurements from circumferentially distributed sensors and a set of pre-computed theoretical dispersion curves. The recorded data are Fourier transformed into a frequency–wavenumber representation
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FW-UAV fault diagnosis based on knowledge complementary network under small sample Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Yizong Zhang, Shaobo Li, Ansi Zhang, Xue An
Fixed Wing Unmanned Aerial Vehicles (FW-UAVs) are prone to faults when performing a variety of tasks, which can lead to mission failure and even pose a safety risk. These faults can be recorded by mission-specific time-series flight data, but are very limited. Traditional methods are usually difficult to process these data, which poses a huge challenge to FW-UAV fault diagnosis (FD). To address this
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Bistable energy-harvesting track nonlinear energy sink in offshore wind turbines Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Qinlin Cai, Yingyu Hua, Songye Zhu, Xihong Zhang, Haoran Zuo
Energy harvesting is particularly attractive for offshore structures owing to power supply challenges in offshore regions. Offshore structures simultaneously experience substantial vibrations from combined wind-wave loads, thus necessitating structural vibration control. This study investigates the feasibility of a bistable energy-harvesting track nonlinear energy sink (EHTNES) designed for offshore
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Phase change enabled high bandgap tunability in graphene-reinforced phononic crystals Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Liangteng Guo, Shaoyu Zhao, Jie Yang, Sritawat Kitipornchai
Highly tunable bandgaps of phononic crystals are of great significance in practical engineering applications. Solid-liquid transitions and graphene reinforcement are two efficient strategies to manipulate material properties, but their potential to enable bandgap tunability remains rarely explored. This study introduces phase-change materials with solid–liquid transition into phononic crystals containing
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Asynchronous Kalman filtering for dynamic response reconstruction by fusing multi-type sensor data with arbitrary sampling frequencies Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Zimo Zhu, Songye Zhu
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Deep learning and structural health monitoring: Temporal Fusion Transformers for anomaly detection in masonry towers Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Fabrizio Falchi, Maria Girardi, Gianmarco Gurioli, Nicola Messina, Cristina Padovani, Daniele Pellegrini
Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. The Temporal Fusion
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Passive control of a composite laminated truncated conical shell via embedded NiTiNOL-steel wire ropes Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-12 Ji-Ren Xue, Ye-Wei Zhang, Mu-Qing Niu, Walter Lacarbonara, Li-Qun Chen
A NiTiNOL-steel wire rope is proposed as a nonlinear passive damper to suppress the vibration of a composite laminated truncated conical shell under harmonic and random excitations. The equation of motion of a composite laminated truncated conical shell hosting four NiTiNOL-steel wire ropes is derived from the generalized Hamilton principle and the first-order shear deformation theory under the assumption
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Sound-vibration spectrogram fusion method for diagnosis of RV reducers in industrial robots Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-04-11 Yuting Qiao, Hongbo Wang, Junyi Cao, Yaguo Lei
Due to continuous high dynamic load of industrial robots, rotate vector (RV) reducers may be more prone to wear and pitting and there will be abnormal vibration and sound caused by incipient faults. However, many experienced experts can identify the health state of machines by the sensitivity of their hearing to some special sounds. To simulate human hearing and visualize the sound heard by experts