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Hybrid uncertainty propagation for mechanical dynamics problems via polynomial chaos expansion and Legendre interval inclusion function Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-16 Liqun Wang, Chengyuan Guo, Fengjie Xu, Hui Xiao
This paper investigates a non-intrusive hybrid uncertainty propagation framework in mechanical dynamic systems, utilizing polynomial chaos expansion (PCE) and Legendre inclusion function. Uncertainties with substantial knowledge and information are conceptualized as stochastic parameters and described using PCE, while Legendre polynomials are employed to represent uncertain, bounded parameters, specifically
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Study of the kurtoses transmission of linear structures under multiple correlated stationary non-Gaussian random loadings using the high-order spectrum method Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-16 Song Cui, Liguo Zang, Lei Hong, Yuxing Bai
The kurtoses of stress responses have significant influences on the fatigue life of in-service structures. To estimate the potential fatigue damage based on excitation information, the kurtoses transmission through linear structures have been studied. However, much of the research up to now has been restricted on the single-input dynamic models. The transmission of kurtosis from multiple input forces
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Detection of ballastless track interlayer gap based on vehicle’s multivariate dynamic response and deep learning Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Shuaijie Miao, Liang Gao, Fanjun Nian, Hong Xiao, Tao Xin, Yanglong Zhong
To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data
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A 3-DOF Multi-Mode spherical actuator driven by cooperative piezoelectric units Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Jiru Wang, Chuang Wang, Langlang Yan, Xiaopeng Liu, Chi Zhang, Hongwei Zhao
Stick-slip piezoelectric actuators are widely used in precision engineering due to their high accuracy and small size. However, most of these actuators achieve multi-DOF motion by serially connecting multiple units, resulting in a non-compact structure and large assembly errors.To resolve this issue, this paper proposes a spherical actuator consists of a rotor and four drive units distributed around
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Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Zongliang Xie, Jinglong Chen, Zhen Shi, Shen Liu, Shuilong He
Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement
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A novel method of vehicle height control utilizing semi-active actuator Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Li ZhiHong, Yao JiaLing, Shi WenKu, Fang MingXia
Inspired by the physical phenomenon that the balance position of the vehicle body changes due to a suspension system with asymmetric damping, this paper presents a novel method to control the vehicle height utilizing semi-active actuators. The proposed vehicle height control method uses the asymmetric damping of the semi-active actuator to control the raising and lowering of the vehicle body, which
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Vibration suppression of series elastic actuator used for robotic grinding based on reconstructed hybrid optimized input shaper Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Xu Tang, Tianzhu Xun, Jixiang Yang, Han Ding
Vibration suppression of the series elastic actuator (SEA) used for robotic compliant grinding is significant for guaranteeing force control accuracy and stability, which is important for material removal accuracy and surface quality improvement. This paper establishes a multi-feedforward control system to achieve vibration suppression for the SEA. The hybrid optimized input shaper (HOIS), instruction
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Localized rail third-order bending mode causes high-order polygonization of high-speed train wheels Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Yunguang Ye, Sheng Qu, Lai Wei, Dadi Li, Caihong Huang, Jianbin Wang, Zhecheng Tao, Feng Gan, Hao Gao, Bin Zhu, Pingbo Wu, Jing Zeng, Huanyun Dai
The formation mechanism of high-order (mainly 18 ∼ 23) polygonization of high-speed train wheels has been debated for over a decade. Currently, researchers believe that the mechanisms leading to the high-order polygonization of high-speed train wheels mainly include: (A) vibration of bogie components, (B) self-excited vibration of wheel-rail friction, (C) third-order bending (B3) modal vibration of
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Enhanced damage detection by dominant wavenumber filtering in steady-state ultrasonic wavefield imaging Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Hyeonwoo Nam, Bugon Kim, Yeseul Kong, Yinan Miao, Gyuhae Park
In this paper, we introduce a novel dominant wavenumber filtering technique for detecting structural damage in steady-state ultrasonic wavefield analysis. Recent studies have suggested the use of full steady-state wavefields instead of guided wavefields for structural imaging in damage detection. These approaches have demonstrated their effectiveness in identifying various types of structural damage
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Optimization of nonlinear energy sink using Euler-buckled beams combined with piezoelectric energy harvester Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Qingsheng Liu, Haiping Liu, Jun Zhang
In this study, a novel device for both vibration reduction and energy harvesting has been developed by merging an Euler-buckled beam nonlinear energy sink and a piezoelectric energy harvester (PEBNES). To assess the output voltage and power from the PEBNES, a simplified standard energy harvesting (SEH) circuit based upon the equivalent impedance method (EIM) is proposed. The approximate steady-state
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A novel optimization framework using frequency-based substructuring for estimation of linear bolted joint stiffness and damping Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Marie Brøns, Francesco Trainotti, Daniel J. Rixen
Estimating the stiffness and damping coming from a joint is a major challenge. This work proposes a novel framework to estimate the unknown parameters using frequency-based substructuring and a maximum a posteriori optimization approach. The idea is to minimize the discrepancy between the measured responses of an assembled system and the responses obtained by coupling the measured responses of the
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Refined linear chirplet transform for time–frequency analysis of non-stationary signals Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Jingyao Zhang, Yuanfeng Bao, Takayoshi Aoki, Takuzo Yamashita
Time-Frequency Analysis (TFA) stands as a pivotal technique for unraveling the inherent properties of signals, which are omnipresent across natural phenomena. Current methodologies encounter significant challenges in the analysis of non-stationary signals, especially those characterized by closely-spaced or intersecting instantaneous frequencies. In this study, we present the refined linear chirplet
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Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-15 Yanping Yang, Zuo Zhu, Siu-Kui Au
Tracking the temporal variation of the properties of a system is relevant in different settings when data of extended duration is available, e.g., anomaly detection, condition monitoring, and trend identification. One simple approach is to divide the data into non-overlapping segments and then identify the model properties of each segment individually using a time-invariant model within the segment
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Synthetic aperture focusing imaging for defect detection in highly attenuative materials using quasi-static components of ultrasonic longitudinal waves Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-11 Quanqing Lai, Caibin Xu, Gonglin Wang, Mingxi Deng
Considering the fact that the acoustic attenuation increases with the carrier frequency, the attention shifts towards the low-frequency ultrasonic waves in the field of ultrasonic nondestructive testing and structural health monitoring of highly attenuative materials. However, low-frequency ultrasonic waves may sharply reduce the imaging resolution, although it can propagate longer distance. When an
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Vibration suppression performance of parallel magnetic nonlinear energy sinks under impulse excitations Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-10 Muxuan Guo, Lihua Tang, Brian Mace, Daniel J. Inman
The nonlinear energy sink (NES) is one representative nonlinear vibration absorber. Despite the existence of excitation threshold, it has been proven to be superior in terms of working frequency range and/or amplitude of vibration suppression, compared to the linear vibration absorber. In recent years, for single-degree-of-freedom NESs, multistable configurations have been explored. However, currently
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Determinants of pedestrian mediolateral foot placement in walking on laterally-oscillating structures and their consequences for structural stability Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-10 Mateusz Bocian, Hanna Wdowicka, Jeremy F. Burn, John H.G. Macdonald
An active control of foot placement in the frontal plane is required to maintain balance during walking. It has been previously shown that, for walking on stationary structures, the foot is placed at a mediolateral distance from the body centre of mass (CoM) determined by the CoM mediolateral velocity at the instance of heel strike plus some constant offset. However, it is currently unknown whether
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A general modeling framework for large-amplitude 2DOF coupled nonlinear bridge flutter based on free vibration wind tunnel tests Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-10 Kai Li, Yan Han, C.S. Cai, Zhixiong Qiu
This study proposes a general modeling framework for large-amplitude vertical-torsional coupled nonlinear flutter, which may have great potential to be applicable to most bridge decks. The framework mathematically models various nonlinear behaviors (including mechanical nonlinearity, non-wind-induced aerodynamic nonlinearity, amplitude-dependent flutter complex mode and wind-induced aerodynamic damping
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Automated structural resilience evaluation based on a multi-scale Transformer network using field monitoring data Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-09 Zepeng Chen, Qitian Liu, Zhenghao Ding, Feng Liu
Structural resilience evaluation is crucial for ensuring structural safety, with structural damage detection (SDD) serving as a core component. Although convolutional neural networks (CNNs) have been proven effective in extracting damage-sensitive features for SDD, their limited receptive field and weak global information processing during feature extraction can lead to insufficient accuracy and reduced
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Efficient simulation method of fully nonstationary stochastic vector processes via generalized harmonic wavelet Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-09 Ding Wang, Ke Chen, Jun Xu, Shan Xu, Fan Kong
Currently, the most common approach to simulate stochastic vector processes for structural dynamic reliability analysis is the Spectral Representation Method (SRM), characterized by a superposition of amplitude-modulated trigonometric functions with random phase angles. However, to represent the vector processes completely, the SRM requires the sampling of a large number of independent random variables
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An identification method of dynamic stiffness and damping for the spindle bearing system of a CNC lathe Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-08 Guanyao Qiao, Jiayi Xu, Yimin Zhang, Chunyu Zhao
The dynamic stiffness and damping of spindle bearings have a decisive role in the machining accuracy of machine tools. This paper proposes an identification method of the dynamic stiffness and damping of spindle bearings using motion errors. Firstly, detecting tests are carried out to obtain the synchronous errors of the spindle at different angular velocities. Next, Lagrange’s equation is applied
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Low-frequency chatter suppression for robotic milling using a novel MRF absorber Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-07 Maxiao Hou, Hongrui Cao, Junqi Ren, Jianghai Shi, Jiang Wei
Robotic milling has a unique advantage for large and complex parts. However, it is extremely prone to low-frequency chatter due to the robot structure mode. In robotic milling, low-frequency chatter has a huge impact on machining quality and efficiency. In this paper, a novel MRF (Magnetorheological fluid) absorber is used to suppress low-frequency chatter during robotic milling based on the proposed
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Attractor based performance characterization and reliability evolution for electromechanical systems Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-07 Wen-Bin Chen, Xiao-Yang Li, Rui Kang
Electromechanical systems (EMS) possess multi-type, fast-iteration, and customized characteristics in modern industry and are usually required for high reliability. To meet the reliability requirements of EMSs, it is essential to identify the physical relationship between the design parameters and reliability of EMSs, i.e., the physical reduction and holistic ability of reliability. However, current
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Study on vibration mechanism and dynamic characteristics for TBM main bearing defects Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-06 Ao Cao, Hongli Gao, Shuwei Fan, Liang Guo, Zhichao You, Yuncong Lei, Yi Sun, Jigang He
As the key component of TBM main driving system, the main bearing plays an indispensable role both in its service life and the normal operation of the TBM. While prior investigations predominantly focused on static analyses of the main bearing, only a scant few delved into its dynamic characteristics and the vibration mechanism. In order to explore the dynamic characteristics of TBM main bearing, a
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Active pendulation control of hoisting systems of ship-mounted cranes under ocean wave excitations: Principle and experimental study Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-06 Yongtao Zhang, Wenai Shen, Zhentao Long, Yipeng Zhang, Zicao Wang, Zhaokun Zhang, Songye Zhu, Alessandro Stocchino, Huaxia Deng, Hongping Zhu
Large-scale ship-mounted cranes are important facilities for marine construction. However, under harsh sea conditions, the hoisting system of a ship-mounted crane is often subjected to wave loadings, leading to frequent occurrences of large-amplitude resonance. This paper proposes an active control method, termed the active displacement compensation (ADC) approach, designed for suppressing the large-amplitude
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Characteristic value decomposition: A unifying paradigm for data-driven modal analysis Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-06 He-Wen-Xuan Li, Dalton L. Stein, David Chelidze
Data-driven modal analysis is an indispensable means to understand the dynamic behavior of engineered structures and natural systems. It effectively captures active dynamic behavior and embeds them in a set of identified linear modal subspaces, unveiling complex dynamic behavior via the superposition of simpler hierarchical spatiotemporal dynamics. Despite the existence of many methods, there is no
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An efficient Bayesian updating framework for characterizing the posterior failure probability Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-06 Pei-Pei Li, Yan-Gang Zhao, Chao Dang, Matteo Broggi, Marcos A. Valdebenito, Matthias G.R. Faes
Bayesian updating plays an important role in reducing epistemic uncertainty and making more reliable predictions of the structural failure probability. In this context, it should be noted that the posterior failure probability conditional on the updated uncertain parameters becomes a random variable itself. Hence, characterizing the statistical properties of the posterior failure probability is important
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A smart e-scooter with embedded estimation of rear vehicle trajectories for rider protection Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-05 Hamidreza Alai, Woongsun Jeon, Lee Alexander, Rajesh Rajamani
This paper develops an active sensing and estimation system for protecting the rider of an e-scooter from car-scooter collisions. The objective is to track the trajectories of cars behind the e-scooter and predict any real-time danger of car-scooter collision. If the danger of a collision is predicted, then a loud car-horn-like audio warning is sounded to alert the car driver to the presence of the
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Experimental study on in-situ internal stress monitoring of full-size water-lubricated journal bearings by embedded fibre Bragg gratings Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-05 Weibin Wu, Xue Yang, Changgeng Shuai, Linzhou Huang, Zeyun Li
Real-time stress evolution inner water-lubricated bearings (WLBs) in service time is unaware due to its installation position and harsh surroundings on ship, which will be of vital importance for identifying bearing fault such as abnormal wear or frictional noise. In this work, we present an embeddable sensor based on fibre Bragg gratings (FBGs) and applicate it for bearing internal stress characterization
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Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-05 Ling-Feng Mei, Wang-Ji Yan, Ka-Veng Yuen, Michael Beer
Transmissibility function (TF) is widely applied in damage detection due to its sensitivity to damage and robustness to external excitations, but its application in online damage detection is rarely reported due to challenges in handling data streams. This study proposes a new TF-based online damage detection method that integrates a truncation-free variational inference-based full Dirichlet process
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Single accelerometer-based inter-story drift reconstruction of soft-story for shear structures with innovative transformation function Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 Kangqian Xu, Miao Cao, Songtao Xue, Dawei Li, Xianzhi Li, Zhuoran Yi
The health monitoring of soft-story is vital during earthquakes. Inter-story drift can reflect the structural damage state and provide evidence for assessment. However, installing numerous accelerometers in buildings is labor and cost intensive. This study proposes an innovative transformation function to estimate the inter-story drift of soft-story of shear structures using only a single accelerometer
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Robust optimized weights spectrum: Enhanced interpretable fault feature extraction method by solving frequency fluctuation problem Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 Yu Wang, Dong Wang, Bingchang Hou, Siliang Lu, Zhike Peng
Machine condition monitoring (MCM) plays a pivotal role in ensuring the reliability, safety, and efficiency of a production and operation system. Fault feature extraction (FFE), as an important step within MCM, aims to filter out interference components (ICs) and extract fault components (FCs) from raw signals. Consequently, it facilitates incipient fault detection and performance degradation assessment
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An adjustable stiffness vibration isolator implemented by a semicircular ring Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 He Ba, Mu-Qing Niu, Li-Qun Chen
Nonlinear vibration isolators with quasi-zero-stiffness characteristics offer broadband vibration isolations, while passive quasi-zero-stiffness isolators are unable to adapt to variable working conditions. To address the issue, a nonlinear semi-active vibration isolator based on a semicircular ring structure is proposed. As an elastic component, the semicircular ring exhibits nonlinear stiffness under
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Ultrasonic measurement method for three-dimensional assembly stress of aero-engine rotors Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 Enxiao Liu, Yongmeng Liu, Jiubin Tan, Wenhao Gu, Jinde Zheng, Shuchao Deng
The precise measurement of three-dimensional stress in the rotor is a key means to ensure the assembly accuracy and operational safety of aero-engines. The unclear coupling influence mechanism of different directions of stress on ultrasonic propagation makes it challenging to establish a three-dimensional stress measurement model. This paper proposes a three-dimensional assembly stress measurement
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A remaining useful lifetime prediction model for concrete structures using Mann-Whitney U test state indicator and deep learning Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 Tuan-Khai Nguyen, Zahoor Ahmad, Duc-Thuan Nguyen, Jong-Myon Kim
This study proposes a framework for predicting the remaining useful lifetime (RUL) of concrete structures using acoustic emission (AE) data. This framework presents two primary contributions: state indicator (SI) construction based on the Mann-Whitney test (MWUT) and RUL prognosis using the damage accumulation indicator (DA) calculated from the SIs. The proposed indicators display an excellent description
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Torsional characteristics optimization of a damper with a torque limiter in a hybrid electric vehicle based on multi-DOF powertrain bond graph modeling Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-02 Zhengfeng Yan, Shaofei Liu, Jiahao Zhang, Gaihua Li, Mingyao Yao, Nong Zhang
Dampers with torque limiters (DTLs) are widely used components in hybrid powertrain systems because they can effectively mitigate torsional vibration problems. In this paper, a multiple-degree-of-freedom (multi-DOF) powertrain bond graph modeling method is proposed to optimize the torsional characteristics of a DTL in a compound planetary set power-split hybrid electric vehicle (HEV). First, the structure
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The coupling of SH guided wave and Lamb wave in the three-dimensional waveguides with finite cross-section Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-01 Songtao Hu, Guofu Zhai, Zhichao Li, Zhengyang Qu, Chao Lu
The waveguides with finite cross-section (such as rail, flat steel) are one of the most crucial configurations in infrastructure. When the shear horizontal (SH) guided waves are excited, the three-dimensional deformation of the elastomer will excite weak Lamb waves. In the finite cross-section waveguides, Lamb waves and SH waves are coupled together with each other. Mode coupling will influence the
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DF-CDM: Conditional diffusion model with data fusion for structural dynamic response reconstruction Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-01 Jiangpeng Shu, Hongchuan Yu, Gaoyang Liu, Yuanfeng Duan, Hao Hu, He Zhang
In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the applicability of SHM techniques, such as condition assessment and damage identification. The current mainstream data-driven method, generative adversarial networks (GAN), suffers from convergence difficulty, limiting the accuracy and efficiency of response reconstruction. In this study, a conditional diffusion
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The use of model-based voltage and current analysis for torque oscillation detection and improved condition monitoring of centrifugal pumps Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-01 Yuejiang Han, Jiamin Zou, Bo Gong, Yin Luo, Longyan Wang, Alexandre Presas Batlló, Jianping Yuan, Chao Wang
Condition Monitoring is essential for the early fault detection and the enhancement of operational efficiency in centrifugal pumps. Motor current signature analysis (MCSA) is a well-established non-intrusive technique for monitoring motors and driven equipment. However, the monitoring results of the MCSA can be affected by both system faults and variations in supply voltage. In this study, a novel
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Adaptive Convergent Visibility Graph Network: An interpretable method for intelligent rolling bearing diagnosis Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-01 Xinming Li, Yanxue Wang, Shuangchen Zhao, Jiachi Yao, Meng Li
In the domain of mechanical equipment maintenance, the necessity for efficient and accurate fault diagnosis is critical. Traditional Graph Neural Network (GNN) methods, which employ time-series data for fault diagnosis, have proven effective but are far from perfect. Their common pitfall lies in mapping time-series data into graph data, often leading to loss of crucial temporal information and computational
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Hybrid fault diagnosis method for underwater thrusters based on the common features of multi-source signals Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-08-01 Shuang Gao, Ying Wang, Zhiyao Zhang, Bingsen Wang, Bo He, Enrico Zio
Integrating the motor and driver into the confined space of an underwater thruster’s sealed shell can lead to current sensor failures, primarily due to high temperatures, pressures, and electromagnetic interference. Despite progress in distinguishing sensor malfunctions from propeller issues, a significant gap exists in understanding simultaneous sensor and propeller failures. This study addresses
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Smart DIC: User-independent, accurate and precise DIC measurement with self-adaptively selected optimal calculation parameters Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Jianhui Zhao, Bing Pan
Existing subset-based digital image correlation (DIC) must rely on user-selected key calculation parameters (i.e., subset size and shape function) to proceed with displacement/deformation analysis. However, the lack of clear guidelines for selecting these parameters leads to varying choices among users, thus introducing artificial uncertainty in DIC measurements. Previous theoretical analyses and experimental
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A wavelet packet deep learning model for Energy-Based structural collapse assessment under Earthquake-Fire Scenarios: Framework and hybrid simulation Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Yuxuan Tao, Zhao-Dong Xu, Yaxin Wei, Xin-Yu Liu, Xulei Zang, Shi-Dong Li
An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy
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A unified sensor and actuator fault diagnosis in digital twins for remote operations Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Agus Hasan, Pierluigi Salvo Rossi
This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements
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Non-convex sparse regularization via convex optimization for blade tip timing Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Kai Zhou, Yanan Wang, Baijie Qiao, Junjiang Liu, Meiru Liu, Zhibo Yang, Xuefeng Chen
Blade Tip Timing (BTT), an emerging technology poised to replace strain gauges, enables contactless measurement of rotor blade vibration. However, the blade vibration signals measured by BTT systems often suffer from significant undersampling. Sparse reconstruction methods are instrumental in addressing the challenge of undersampled signal reconstruction. However, traditional approaches grounded in
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Total harmonic distortion estimation in piezoelectric micro-electro-mechanical-system loudspeakers via a FEM-assisted reduced-order-model Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Chiara Gazzola, Alberto Corigliano, Valentina Zega
Piezoelectric micro-electro-mechanical-system (MEMS) loudspeakers are attracting growing research interest in the last years due to the increasing interest towards miniaturization required by new wireless audio devices. Finite Element Models (FEM) and Lumped Element Models (LEM) able to accurately simulate their linear response have been recently proposed in the literature. However, a nonlinear model
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Enhanced acoustic attenuation in a coiled meta-silencer: Broadband low-frequency noise control through rainbow trapping Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-31 Soo-Seong Lee, Jun-Young Jang, Kyungjun Song
In this study, we introduce a -silencer designed for broadband low-frequency acoustic attenuation. Composed of four parallel resonators, each containing a coiled multi-slit, it employs rainbow trapping to realize effective low-frequency acoustic attenuation. The curved multiple slits produce an acoustic black hole effect that significantly assists in noise reduction within the low-frequency range.
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Nonlinear wire rope isolator with magnetic negative stiffness Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Ying Zhang, Yaguo Lei, Junyi Cao, Qinghua Liu, Wei-Hsin Liao
The high-static-low-dynamic-stiffness (HSLDS) isolators have been widely employed to achieve low-frequency vibration isolation. However, traditional HSLDS isolators are prone to nonlinear jump phenomena due to the introduction of nonlinear stiffness characteristics, which will cause the performance deterioration under high excitation levels. To overcome this issue, inspired by the dry friction characteristic
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Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Wenyu Huo, Zuhua Jiang, Zhipeng Sheng, Kun Zhang, Yonggang Xu
Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope
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On decision-theoretic model assessment for structural deterioration monitoring Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Nicholas E. Silionis, Konstantinos N. Anyfantis
As data from monitored structures become more available, the demand for its efficient use in structural operation and management grows. This can be achieved by using structural response measurements to assess the usefulness of models describing deterioration processes and the mechanical behaviour of structures. This work aims to frame Structural Health Monitoring as a Bayesian model updating problem
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A cascaded control strategy for magneto-rheological dampers based on Hammerstein model Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Shuyou Yu, Jie Guo, Xinze Xu, Songlin Zhang, Baojun Lin
In order to effectively attenuate the inherent hysteresis nonlinearity of magneto-rheological (MR) dampers, and achieve precise tracking control of damping force, a cascaded control strategy based on Hammerstein model is proposed in this paper. A BP neural network is utilized to construct the nonlinear module of Hammerstein model, which accurately captures the hysteresis behavior of MR dampers. The
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Data-driven structural identification of nonlinear assemblies: Asymmetric stiffness and damping nonlinearities Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Sina Safari, Julián M. Londoño Monsalve
Nonlinear model identification for mechanical structures is a challenging task, particularly when the structure exhibits asymmetric nonlinear behaviour related to both stiffness and damping. In this paper, a new method for parametric nonlinear model identification of structures from measured data is proposed by cascading optimisation problems defined for model selection and parameter estimation steps
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Comprehensive identification of wheel-rail forces for rail vehicles based on the time domain and machine learning methods Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-30 Tao Zhu, Xiaorui Wang, Jiaxin Wu, Jingke Zhang, Shoune Xiao, Liantao Lu, Bing Yang, Guangwu Yang
Wheel-rail force is an essential indicator for evaluating rail vehicles’ operational safety. This study combined the traditional time domain identification method for dynamic loads and the machine learning method to rapidly identify the wheel-rail forces for rail vehicles and evaluate the operational safety of rail vehicles. Firstly, a vertical dynamic model of rail vehicles considering the nonlinearity
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Structural modal parameter identification with the Power-Exponential window function Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-29 Jilin Hou, Dengzheng Xu, Łukasz Jankowski
In view of the demand for accurate modal identification, and based on the characteristics of free vibration response, this paper introduces a new window function for Fourier Transform called the Power–Exponential window. The Power–Exponential window addresses the characteristics of free vibration response. It significantly enhances the accuracy of modal identification by improving the spectral properties
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Improving the performance of vibration energy harvesting from weak excitations by a lever-type mechanism Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-29 Mengjie Shang, Weiyang Qin, Kuan Lu, Qi Liu, Haitao Li
It is difficult to efficiently harvest the energy of weak and broadband vibrations existing in the environment. In this study, a piezoelectric vibration energy harvester incorporating a lever structure and bi-stability is proposed. Under base excitation, the lever can amplify the displacement and make the piezoelectric beam execute a bi-stable snap-through motion easily, thereby producing large electric
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Kurtosis and crest factor simultaneous control for non-Gaussian random vibration test Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-29 Ronghui Zheng, FeiFei Chen, Guoping Wang, Fufeng Yang
Non-Gaussian random vibration tests have received increasing attention in the field of dynamic environmental testing. This paper presents a kurtosis and crest factor simultaneous control strategy for producing the preset stationary non-Gaussian random vibration environment. A novel generation method of non-Gaussian random vibration signal is proposed, which is composed of the shock and random signal
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Automatic mode tracing of dispersion relations for guided waves in elastic waveguides via physics-driven affinity propagation (AP) clustering Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-29 Xudong Yu, Bohan Liu, Hai Shen, Peng Zuo, Zheng Fan
Guided ultrasonic waves are attractive screening tools for elongated engineering structures due to their ability to propagate over long distances and flexibility in selecting mode-frequency combinations. Both computing dispersion solutions and accurately tracing them into dispersion curves are essential for guided waves’ non-destructive evaluation (NDE) and structural health monitoring (SHM) applications
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Study on the torsional stiffness and vibration response law of laminated coupling considering the effect of excess Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-29 Tao Li, Zhiqiang Huang, Zhen Chen, Jie Wang, Cheng Wang
The torsional stiffness model of the coupling, considering the influence of excess, is established, and the influence of structure-excess parameters on the vibration stability of the coupling is studied. Firstly, the torsional stiffness model of the coupling considering the influence of excess is constructed by using finite element and response surface theory, and it is verified that the model can
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An interpretable TFAFI-1DCNN-LSTM framework for UGW-based pre-stress identification of steel strands Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-28 Longguan Zhang, Junfeng Jia, Yulei Bai, Xiuli Du, Binli Guo, He Guo
Steel strands serve as the key load-bearing components of pre-stressed bridges, yet the identification of effective pre-stress for steel strands is a challenging task. In this study, a time and frequency adaptive fusion input-one dimensional convolutional neural network-long short-term memory (TFAFI-1DCNN-LSTM) framework was proposed for ultrasonic guided wave (UGW)-based effective pre-stress identification
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Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-27 Cailu Pan, Zhiwu Shang, Lutai Tang, Hongchuan Cheng, Wanxiang Li
In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable
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LSTA-Net framework: Pioneering intelligent diagnostics for insulating bearings under real-world complex operational conditions and its interpretability Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2024-07-27 Tongguang Yang, Guanchen Li, Yicheng Duan, Hui Ma, Xuejun Li, Qingkai Han
Deep Learning has been attracting considerable attention as it can autonomously learn important signal features and has shown great potential for fault diagnosis. However, given the variability of high-power variable-frequency industrial systems, especially under unfavorable service conditions such as voltage and load fluctuations, the identification of insulated bearing faults in variable-frequency