Mechanical Systems and Signal Processing最新文献

{"title":"Ultra-low frequency air flotation vibration isolation system with a dual-chamber structure using adaptive control strategy","authors":"Tianyi Li,&nbsp;Shilong Guo,&nbsp;Zhendong Lan,&nbsp;Bo Zhao,&nbsp;Jiubin Tan,&nbsp;Chenglong Yu","doi":"10.1016/j.ymssp.2026.113987","DOIUrl":"10.1016/j.ymssp.2026.113987","url":null,"abstract":"<div><div>Vibration isolation systems for ultra-precision instruments are strongly influenced by internal resonances, leading to an increase in vibration transmissibility of up to 10–30 dB at the resonance frequencies. The dual-chamber air-floating vibration isolation system exhibits an extremely low natural frequency. However, the presence of the expansion chamber introduces internal resonance problems at mid-to-high frequencies. To enhance the vibration isolation performance of the dual-chamber air-floated isolation system, this paper proposes an adaptive control strategy tailored to such systems to address internal resonance beyond the natural frequency. The dual-chamber air-floated isolation system is accurately modeled and systematically analyzed in this paper. The results reveal that the fundamental cause of internal resonance in the dual-chamber isolation system is Helmholtz resonance. To address this issue, a novel orthogonal basis function infinite impulse response (OBF-IIR) controller is designed in this paper to efficiently compensate for vibrations induced by the dual-chamber Helmholtz resonance effect. On this basis, a fast, accurate online adaptive algorithm is developed to update the controller zeros in real time, enabling adaptive, synchronous compensation of internal resonances in the dual-chamber isolation system. The proposed OBF-IIR controller not only suppresses internal resonances induced by the spring–damper model and the dual-chamber Helmholtz resonance effect, but also compensates for resonances arising from other sources. The proposed adaptive control strategy demonstrates faster convergence and higher accuracy, reducing the vibration transmissibility of the isolation system by 10–30 dB in the 2–100 Hz range and decreasing the cumulative power spectral density at 100 Hz by 23.8%–84.9%.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"248 ","pages":"Article 113987"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric design enables self-coupled locally resonant metastructure for multi-modal vibration isolation","authors":"Xiaowei Zhang,&nbsp;Xiaopeng Wang,&nbsp;Yingrui Ye","doi":"10.1016/j.ymssp.2026.113984","DOIUrl":"10.1016/j.ymssp.2026.113984","url":null,"abstract":"<div><div>Symmetry is commonly used in engineering design for its simplicity and structural stability. Conventional locally resonant metastructures with strict spatial symmetry exhibit only one active mode, limiting modal diversity and dynamic performance. To overcome this constraint, we introduce spatial stiffness asymmetry, enabling three-dimensional dynamic responses. Such asymmetric design induces coupling between translational and rotational degrees of freedom, allowing multiple resonant modes to be excited by a single-directional input. Leveraging this mechanism, we design a metastructure that achieves vertical vibration isolation through three distinct coupled modes generated by a single resonator. A theoretical model is developed to describe the asymmetric self-coupling behavior, and vibration-table experiments confirm the predicted multi-band isolation performance. This work provides a new strategy for enhancing modal utilization in resonant systems and offers practical guidance for compact, multi-band vibration control.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"248 ","pages":"Article 113984"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks based digital volume correlation for displacement and strain measurements","authors":"Zhuhong Wang,&nbsp;Hang Zhou,&nbsp;Hanlong Liu","doi":"10.1016/j.ymssp.2026.113998","DOIUrl":"10.1016/j.ymssp.2026.113998","url":null,"abstract":"<div><div>Accurate measurement of three-dimensional deformation behavior is critical for understanding material mechanical properties. However, traditional Digital Volume Correlation (DVC) methods are limited by discrete sub-volume discretization, lack of physical constraints, and low computational efficiency. Data-driven approaches cannot guarantee physical plausibility and depend on large quantities of densely sampled data. This study proposes a novel physics-informed deep learning method for DVC (PiNetDVC). The method takes spatial coordinates as inputs and simultaneously predicts displacement and strain fields through continuous function representation, overcoming spatial resolution limitations and data dependency. The strain field is directly incorporated as a network output, with strain–displacement compatibility enforced by comparing network-predicted strains with strains derived from displacement gradients. A unified loss function integrates image consistency constraints with physics-informed regularization. Validation on six scenarios demonstrates superior performance over traditional ALDVC, achieving accuracy improvements of 81%, 83%, and over 95% for rigid body translation, uniaxial tension, and shear band deformation, respectively. For complex deformation patterns such as sinusoidal and non-uniform star-shaped modes, errors are maintained at the order of 10^-3. Stable accuracy is maintained under 20 dB noise, with robust performance across different architectures and loss configurations. PiNetDVC provides an effective solution for 3D deformation measurement in aerospace, mechanical, and civil engineering applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"248 ","pages":"Article 113998"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated alternating iterative identification for multiple moving vehicle loads based on Anderson acceleration with safeguard strategy","authors":"Bohao Xu,&nbsp;Ling Yu,&nbsp;Zhenhua Nie","doi":"10.1016/j.ymssp.2026.113979","DOIUrl":"10.1016/j.ymssp.2026.113979","url":null,"abstract":"<div><div>As one of the challenging topics in structural health monitoring, the identification of multiple moving vehicle loads remains largely unexplored owing to the large differences in load magnitudes. Even though a recent study introduced multiple regularization parameters (MRP) within a two-stage framework to distinguish the properties of different loads, its performance is highly sensitive to the initial estimates and deteriorates as the number of loads increases. To address this, the original two-stage work is extended into an alternating iterative framework (AIF), which iteratively updates the static load, dynamic load, and the variance of the dynamic loads. This extension follows the conclusion in the previous study that the regularization parameters chosen within the reasonable range of residual noise are close. Furthermore, Anderson acceleration is introduced only to the static load and the variance of dynamic load to enhance effectiveness. A safeguard strategy is incorporated to ensure the local convergence of the AIF. Finally, the proposed method is validated in both numerical simulations and laboratory experiments. The comparative cases under different response combinations, different numbers of loads and different initial estimates in the numerical simulations show that the proposed method achieves a higher accuracy, especially in comparison with the previous study. The SNR threshold required for maintaining reliable identification decreases from 25 dB to 20 dB, even when the noise variance is inaccurately estimated. Moreover, the weight of the model vehicle can be reasonably estimated by the proposed method in the validation of experiment.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"248 ","pages":"Article 113979"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-mechanism-based digital twin system for intelligent contour error compensation of ultra-precision machining","authors":"Chengyi Wu ,&nbsp;Shijun Ji ,&nbsp;Ji Zhao ,&nbsp;Enzhong Zhang ,&nbsp;Guang Yang","doi":"10.1016/j.ymssp.2026.113982","DOIUrl":"10.1016/j.ymssp.2026.113982","url":null,"abstract":"<div><div>In the implementation of digital twin for ultra-precision machining (UPM) based on deep learning, conventional approaches suffer from limited interpretability of model and insufficient visualization capabilities. Moreover, their performance is significantly compromised by the coupling effects of multisource errors, making it difficult to achieve accurate position prediction and effective compensation. To address these limitations, this paper proposes a novel digital twin system which is driven by a hybrid model that integrates the Patch Time Series Transformer and multisource error coupling mechanism, and enables the visualization of the error compensation strategy. It achieves intelligent contour error compensation during machining by dynamically correcting the position commands along the trajectory. Based on an analysis of the theoretical error band arising from the multisource error coupling mechanism, the position prediction accuracy of each axis is improved through the self-supervised learning and hyperparameter fine-tuning methods. Furthermore, temporal stability is validated via time-effect analysis. Comprehensive case studies are conducted on a custom-built multi-axis ultra-precision machine tool, covering both single-axis and multi-axis motions under varying loads, feedrates, and ambient temperatures. The test results demonstrate that the proposed method improves single-axis positioning accuracy by 47.07% and multi-axis trajectory contour accuracy by 26.99%. In the micro-groove machining experiment, the compensated linear positioning error is reduced to 0.0393 μm, and the angular positioning error is 0.0013°, with the resultant cutting force indirectly reduced by up to 9.20%. The robustness and adaptability of the proposed method are validated under complex operating conditions, thereby enabling high-accuracy contour control in practical UPM applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"248 ","pages":"Article 113982"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FRF-based crack localization in AMB-Supported rotors using neural networks","authors":"Giovanni Donati ,&nbsp;Chiara Camerota ,&nbsp;Marco Mugnaini ,&nbsp;Michele Basso ,&nbsp;Jerzy T. Sawicki","doi":"10.1016/j.ymssp.2026.113939","DOIUrl":"10.1016/j.ymssp.2026.113939","url":null,"abstract":"<div><div>Well-established procedures exist for monitoring and diagnosing faults in rotating machinery, and many techniques for detecting rotor cracks have been explored in the literature. However, limited progress has been made in developing non-invasive methods capable of accurately localizing rotor cracks and assessing their severity without requiring rotor disassembly or direct physical inspection.</div><div>This paper presents a novel, non-invasive approach for crack localization in flexible rotors supported by Active Magnetic Bearings (AMBs), based exclusively on frequency responses acquired through AMB excitation. The methodology involves constructing a physics-informed fault dictionary using frequency responses simulated on a high-fidelity digital twin of the rotor system, obtained through established modeling procedures, under various crack locations and severities. These responses exhibit characteristic shifts in resonance and antiresonance frequencies, which are used to define distinct fault classes.</div><div>Neural network classifiers were trained on the simulated dataset, with a 1D Convolutional Neural Network (1D-CNN) used as the primary model and an Autoencoder + Multilayer Perceptron (AE + MLP) used as a comparative baseline, to evaluate their ability to automatically identify the fault zone. The entire framework was validated experimentally on a dedicated AMB-supported test rig, confirming the ability of the proposed method to detect and localize cracks without requiring additional sensors or plant disassembly. The 1D-CNN achieved a classification accuracy of 99.4% on simulated test data, while the AE + MLP baseline reached 98.3%. Experimental validation on a dedicated AMB-supported test rig showed correct localization for all tested crack cases.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"247 ","pages":"Article 113939"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model updating method based on computer vision and autocorrelation sensitivity: Deep integration of visual information and physical mechanisms","authors":"Weijia Liu ,&nbsp;Changhai Zhai ,&nbsp;Weiping Wen ,&nbsp;Kun Liu","doi":"10.1016/j.ymssp.2026.113964","DOIUrl":"10.1016/j.ymssp.2026.113964","url":null,"abstract":"<div><div>Traditional structural health monitoring relies on sensor deployment but is constrained by high installation costs, insufficient monitoring network coverage, and noise interference in complex seismic scenarios, limiting its application. Leveraging existing surveillance cameras in buildings for non-contact monitoring emerges as a promising solution. This study proposes a finite element model updating method integrating computer vision with time-domain signal autocorrelation sensitivity. This method deeply integrates visual displacement data from surveillance videos with structural mechanics models, employing the autocorrelation function of time-domain signals for effective noise reduction. It enhances the identification of local stiffness changes, thereby significantly improving the accuracy and robustness of model updating. This study first conducts model updating through numerical simulation methods. The displacement autocorrelation sensitivity method is employed, systematically accounting for measured response noise, seismic motion noise, and uncertainties in seismic motion (including spectral characteristics, duration, and peak ground acceleration). Numerical simulation results demonstrate that, under structural response and seismic motion noise conditions with a signal-to-noise ratio (SNR) as low as 20 dB, the displacement autocorrelation sensitivity method achieves a parameter updating error within 5%, validating its high adaptability and robustness in complex disturbance environments. For far-field non-impulsive seismic motions, the displacement autocorrelation sensitivity method exhibits higher precision and stability compared to traditional displacement sensitivity methods. For engineering feasibility assessment, shaking table tests were conducted on a three-story steel frame, integrating displacement time histories from indoor/outdoor camera videos with ground motion data from IMU sensors for model updating. Test results show Pearson correlation coefficients of 0.91, 0.94, and 0.97 for displacement time history predictions versus measured values from the top to the first story, with peak displacement relative errors below 6% for all stories. This method can efficiently utilize existing building surveillance videos to complete model updates within minutes in post-earthquake environment, providing reliable support for damage assessment and emergency response.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"247 ","pages":"Article 113964"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic response and sliding mode control of a cold rolling mill subjected to harmonic and Gaussian colored noise excitations","authors":"Xiaofei Chen ,&nbsp;Wei Zhang ,&nbsp;Yufei Zhang","doi":"10.1016/j.ymssp.2026.113971","DOIUrl":"10.1016/j.ymssp.2026.113971","url":null,"abstract":"<div><div>During operation, cold rolling mills are susceptible to the coupled effects of random excitations and structural nonlinearities, which can induce complex dynamic behaviors that adversely affect rolling quality and equipment safety. This paper studies the structural dynamic characteristics and vibration suppression for a two-degree-of-freedom cold rolling mill vertical structure model under combined harmonic and random excitation for the first time. Firstly, an averaging method and a stochastic method are extended to derive the amplitude-frequency and steady-state response equations, respectively. Secondly, the response shows the mill exhibits nonlinear hard spring characteristics and bistability in the resonance region. The coexistence and evolution of low- and high-amplitude attractors are further elucidated via the equivalent potential energy diagram and basin of attraction. Additionally, random excitation is a key factor inducing chaotic behavior in the rolling mill. Finally, Gaussian colored noise induces stochastic switching, stochastic P- and D-bifurcations. This can lead to defects in the rolled products, and in severe cases, it may even threaten the safe operation of the rolling mill. To suppress this catastrophic switching, this paper innovatively introduces the improved double power exponential reaching law to design sliding mode control, achieving faster convergence, suppressing chattering and reducing energy consumption. The proposed control has been rigorously proven to be stable and has been effectively verified through numerical simulations. The research findings provide essential theoretical foundations and technical support for the safe design and manufacture of vertical structural models for cold rolling mills in engineering practice.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"247 ","pages":"Article 113971"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the prediction of squeal through multiphysic and multiscale analysis of experimental data from a pin-on-disk system","authors":"Sacha Durain ,&nbsp;Quentin Caradec ,&nbsp;Mathis Briatte ,&nbsp;Jean-François Brunel ,&nbsp;Cédric Hubert ,&nbsp;Franck Massa ,&nbsp;Philippe Dufrenoy","doi":"10.1016/j.ymssp.2026.113968","DOIUrl":"10.1016/j.ymssp.2026.113968","url":null,"abstract":"<div><div>Brake squeal remains a major challenge for the automotive industry due to its transient nature and its multiphysics, multiscale characteristics. This study advances the understanding and prediction of these dynamic instabilities through experimental analysis using an instrumented pin-on-disk system. The proposed multiphysics and multiscale framework reveals a correlation between the onset of dynamic instability and contact localization during a friction test. The results indicate that contact localization shaped by wear history and thermomechanical evolution plays a key role in triggering the instability.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"247 ","pages":"Article 113968"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aligned sparse non-negative matrix factorization for vehicle-track features decoupling","authors":"Jiyuan Huo ,&nbsp;Jianwei Yang ,&nbsp;Dechen Yao ,&nbsp;Zhongshuo Hu ,&nbsp;Yuanting Dai ,&nbsp;Bin Zhu","doi":"10.1016/j.ymssp.2026.113907","DOIUrl":"10.1016/j.ymssp.2026.113907","url":null,"abstract":"<div><div>Vibration signals collected from in-service urban rail vehicles exhibit strong coupling between vehicle dynamics and track geometry excitations, often compounded by environmental noise. This poses a significant challenge for the accurate decoupling of sources and the estimation of track geometric parameters, particularly curve superelevation, from vehicle acceleration data. To address this, we propose an Aligned Sparse Non-negative Matrix Factorization (ASNMF) framework to decouple of vehicle-track features: A Kurtosis-Spectral Peak (KSP) criterion is first applied to construct a Hankel matrix that enhances the representation of non-stationary features; A multi-objective optimization is then formulated by integrating a Gini-based sparsity constraint and a Maximum Mean Discrepancy (MMD) alignment term to ensure consistent component extraction; The resulting multiplicative updating algorithm yields physically interpretable decompositions. Validation using both simulated and real-world vibration data demonstrates that ASNMF effectively separates vehicle and track-induced responses under strong coupling and noise. Compared with existing matrix factorization and blind source separation methods, ASNMF achieves higher signal fidelity and more accurate track-related feature estimation, offering a robust and novel solution for decoupling and interpreting coupled vehicle–track dynamic responses under non-stationary operating conditions.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"247 ","pages":"Article 113907"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}