样式: 排序: IF: - GO 导出 标记为已读
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Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Yunpeng Liu, Hongkai Jiang, Renhe Yao, Tao Zeng
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Sound field control of duct noise with two sensorless adjustable impedance units Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Zhijun Yu, Daoqing Chang, Yongyuan Zhang, Xiaobin Cheng
This study investigates the sound insulation capabilities of two electromagnetic induction membranes controlled by shunt circuits in the duct sound field. The real and imaginary parts of the diaphragm acoustic impedance can be precisely decoupled and regulated through the operational amplifiers and adjustable resistors in the shunt circuits. The parameter indicators and are also presented to evaluate
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Adaptive filter based anti-strong transient impact method for vortex flowmeter Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Chun-Li Shao, Shi-Wei Wang, Shuang-Long Yang, Ke-Jun Xu, Ze-Xia Huang
The vortex flowmeter is widely used for fluid flow measurement in industrial pipelines. However, the industrial sites may have complex situations, when pipelines are exposed to high-intensity or high-frequency transient impacts, the interference energy will surpass that of the vortex signal, leading to measurement errors in frequency estimation methods relying on the maximum spectrum peak. To address
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Numerical and experimental study on the novel active aerostatic bearings with the controllable throttling effect for obtaining high static stiffness Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Wenjun Li, Shenling Cai, Pengfei Zhang, Yuan Ping, Kai Feng
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Comparison of nonlinear vibration responses induced by edge crack and surface crack of compressor blades Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Hong Guan, Qian Xiong, Hui Ma, Weiwei Wang, Kaixuan Ni, Zhiyuan Wu, Xunmin Yin, Songtao Zhao, Xiaoxu Zhang
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Modal parameter estimation of turbomachinery in operation taking into account friction damping Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-14 Mona Amer, Carlos E. Ventura, Niklas Maroldt, Joerg R. Seume, Joerg Wallaschek
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Improving processing stability of thin-walled casing workpieces by sucker-mounted TMD: Optimization, design, and implementation Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-13 Wenshuo Ma, Yiqing Yang, Jingjun Yu, Guoqing Yang
Passive chatter suppression by addition of tuned mass dampers has proven effective in enhancing machining stability of thin-walled workpieces. However, the damper installed on a fixed position may fail to offer effective and robust chatter suppression, as the cutting position varies during machining. This challenge is compounded by the steep dynamics variation of the workpiece along the radial direction
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A relative path based virtual sensing method for the active feedback headrest system Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-13 Chunyu Liu, Chuang Shi, Yujie Fu, Huiyong Li, Ce Zhu
Virtual sensing methods (VS) enable an active noise control (ANC) system to achieve noise reduction at a target control point, but impractical to place an error microphone for an extended period of time. This is realized by employing remote monitoring microphones. This paper introduces the relative path based VS (RP-VS) method for feedback ANC systems and compares it to existing VS methods, including
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A new approach for sparse optimization with Moreau envelope to extract bearing fault feature Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-13 Tianxu Qiu, Weiguo Huang, Zhichao Zhang, Jun Wang, Zhongkui Zhu
The bearing fault feature detection and extraction from vibration signals are of great significance in mechanical fault diagnosis and machine condition monitoring. Sparse optimization focusing on different domains is one effective tool to extract weak fault feature from strong background noise. To unbiasedly detect fault features, one sparse model is constructed on the time–frequency domain, and Minimax
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Multi-iteration Frequency-domain Synthetic Aperture Focusing Technique (MIF-SAFT) in multi-mode laser ultrasound for image quality improvement Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-13 Kai-Ning Ying, Chen-Yin Ni, Lu-Nan Dai, Wen-Xi Cao, Zhi Yang, Ling Yuan, Wei-Wei Kan, Zhong-Hua Shen
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A novel locally resonance metamaterial cylindrical shell with tower-shaped lattice for broadband vibration suppression Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Shaoke Wan, Lele Li, Gang Wang, Xiaohu Li, Jun Hong
Vibration issues in cylindrical shells are prevalent in engineering applications and can significantly impact the operational performance of systems. To address these challenges, this paper designs a novel local resonance tower structure, and establishes an analytical model to investigate vibration characteristics of the metamaterial cylindrical shell. Based on the model, a gradient design strategy
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An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Xinwei Wang, Yinghao Zhao, Zhihao Wang, Nan Hu
Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme
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Nonlinear dynamic modeling and analysis of the fluid-transporting cracked pipe using the hybrid semi-analytical and finite element method Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Wenhao Ji, Hongwei Ma, Wei Sun, Fangming Liu
The fatigue cracks may develop in the pipe systems due to long-term cyclic loading, which may cause catastrophic accidents. The finite element method (FEM) is usually adopted to simulate the stress singularity and breathing effect, resulting in expensive computational costs. Therefore, in this paper, an efficient hybrid semi-analytical and finite element (SA-FE) method is proposed to study the nonlinear
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Intelligent in-process enhancement technique for machining efficiency in CNC machine tools based on spindle power Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Yeming Jiang, Kuo Liu, Jiadong Huang, Di Zhao, Wei Yang, Yongqing Wang
In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology
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An online chatter detection and recognition method for camshaft non-circular contour high-speed grinding based on improved LMD and GAPSO-ABC-SVM Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Rongjin Zhuo, Zhaohui Deng, Yiwen Li, Tao Liu, Jimin Ge, Lishu Lv, Wei Liu
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Unsupervised complex semi-binary matrix factorization for activation sequence recovery of quasi-stationary sources Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-11 Romain Delabeye, Martin Ghienne, Olivia Penas, Jean-Luc Dion
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Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Sifan Wang, Mayuko Nishio
Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly
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New deterministic model for calculating mesh stiffness and damping of rough-surface gears considering elastic–plastic contact and energy-dissipation mechanism Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Zhou Sun, Siyu Chen, Jinyuan Tang, Zehua Hu, Xuan Tao, Qibo Wang, Shuhan Yang, Ping Jiang
For a long time, meshing parameter calculations and dynamic modeling of gear systems have assumed smooth tooth surfaces, disregarding the impact of actual three-dimensional (3D) microscopic topography. Based on elastic–plastic contact and energy-dissipation mechanisms, this work developed a comprehensive deterministic model that integrates 3D rough surface modeling, reconstruction, and contact analysis
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Double-classifier adversarial learning for fault diagnosis of rotating machinery considering cross domains Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Tongtong Jin, Chuanhai Chen, Jinyan Guo, Zhifeng Liu, Yueze Zhang
Deep learning methods have been demonstrated remarkable success in machine fault diagnosis under the constraint of identical distribution between training datasets and test datasets. However, achieving such conditions in practical scenarios remains challenging. Variations in working conditions lead to distinct distributions in fault data, while acquiring sufficient labeled fault data is often difficult
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Benefits of Mann–Kendall trend analysis for vibration-based condition monitoring Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Adrien Marsick, Hugo André, Ilyes Khelf, Quentin Leclère, Jérôme Antoni
Rank-based statistics offer an appealing framework to non-parametric robust trend detection. Especially, the Mann–Kendall test detects the presence of a trend in a series. This paper investigates the benefits of using the Mann–Kendall test, which seems to have remained largely unnoticed in the context of vibration-based condition monitoring. Two contributions structure the present paper. First, theoretical
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Data fusion based on short-term memory Kalman filtering using intermittent-displacement and acceleration signal with a time-varying bias Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Ashish Pal, Satish Nagarajaiah
Accurate displacement information plays a vital role in various applications from structural health monitoring to damage detection in physical systems. Due to practical reasons, displacement sensing is often performed using sensors that provide less accuracy or a low sampling rate. This study proposes an algorithm for displacement signal estimation that fuses high sampling rate acceleration data (which
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Simulating non-stationary and non-Gaussian cross-correlated fields using multivariate Karhunen–Loève expansion and L-moments-based Hermite polynomial model Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-10 Zhao Zhao, Zhao-Hui Lu, Yan-Gang Zhao
In practical engineering, cross-correlated random fields are often used to model structural materials or random loads containing multiple correlations. Effective and accurate simulation of these cross-correlation fields is an important prerequisite for subsequent reliability analysis and uncertainty quantification of complex systems. Therefore, this paper proposes a new simulation method for non-stationary
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High-accuracy ultrasonic imaging of the defects in the complex-surface components by the search-vector imaging condition Mech. Syst. Signal Process. (IF 8.4) Pub Date : 2024-05-09 Kaipeng Ji, Peng Zhao, Chaojie Zhuo, Hao Chen, Jingdai Wang, Jianzhong Fu
Complex-surface components play more and more significant roles in engineering practice because of the development of manufacturing technology and requirements from various industries. Void and crack defects inside the components greatly deteriorate the mechanical properties, and ultrasonic full-matrix imaging is a promising method to detect the defects. However, the research on high-quality full-matrix
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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