Mechanical Systems and Signal Processing最新文献

{"title":"First excursion probability of dynamical systems: A review on computational methods","authors":"Youbao Jiang ,&nbsp;Xuyang Zhang ,&nbsp;Michael Beer ,&nbsp;Matthias G.R. Faes ,&nbsp;Costas Papadimitriou ,&nbsp;Hao Zhou","doi":"10.1016/j.ymssp.2025.112751","DOIUrl":"10.1016/j.ymssp.2025.112751","url":null,"abstract":"<div><div>The theory of dynamic reliability, predicated on the first excursion failure criterion, holds significant importance in the domains of seismic and wind resistance of structures, as well as in the assessment of the reliability of machinery and airplanes. This theoretical framework offers a mathematical description of failure probabilities, which serve as critical indicators for the safety evaluations of dynamic systems. However, dynamical systems such as large structures, machines or airplanes are composed of numerous members and nodes that may be influenced by uncertainties related to loads, geometric imperfections, and material properties. The inherent high-dimensional randomness and pronounced nonlinear coupling effects contribute to the complexity and implicit nature of the system failure modes in these systems. Consequently, the computation of the first excursion probability for complex dynamical systems presents a formidable challenge that necessitates comprehensive investigation. To summarize the current methodologies, this paper delineates a state-of-the-art review of dynamic reliability theory, with a particular emphasis on its potential to address the first excursion probability in dynamical systems.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112751"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835212","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":"Full-field detection of multi-band structural vibration mode shapes using undersampled video","authors":"Xuefen Xiong ,&nbsp;Lei Liu ,&nbsp;Mengmeng Dang ,&nbsp;Luyi Liang ,&nbsp;Zhi Zhong ,&nbsp;Bin Liu ,&nbsp;Huibo Zhang ,&nbsp;Lei Yu ,&nbsp;Mingguang Shan","doi":"10.1016/j.ymssp.2025.112746","DOIUrl":"10.1016/j.ymssp.2025.112746","url":null,"abstract":"<div><div>Video-based vibration measurement technology has gained widespread recognition across various fields for its high accuracy and spatial resolution. However, the limitations of the Nyquist sampling theorem present challenges for high-frequency vibration measurements. While some studies have advanced using external triggering devices and specific excitation methods, these approaches remain limited to single-frequency or narrow-band vibration measurements. Here, an alias-free undersampling method is proposed for extracting multi-band vibration mode shapes from video. This method uses the natural frequencies of the structure as prior information, and combines band signal undersampling with numerical constraints to enable alias-free sampling of multi-band vibrations. Consequently, it allows the camera to capture video at an undersampled frame rate while preserving full information across multiple frequency bands. By applying the invariant mode shape criterion, the method reconstructs the spatial distribution of multi-band vibrations from the undersampled video, facilitating full-field detection of vibration mode shapes. Unlike existing methods, this approach enables multi-band vibration mode extraction when the natural frequencies are provided, without the need for an expensive high-speed camera or complex auxiliary measurement systems. Both numerical simulations and real-world experiments validate the effectiveness and reliability of this method.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112746"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829518","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":"Fourier neural operator for flow-induced rotordynamics force prediction and application to a SCO2 magnetic bearing-rotor system","authors":"Jongin Yang ,&nbsp;Dongil Shin ,&nbsp;Alan Palazzolo","doi":"10.1016/j.ymssp.2025.112750","DOIUrl":"10.1016/j.ymssp.2025.112750","url":null,"abstract":"<div><div>This study presents a novel approach for rotordynamic fluid–structure interaction (FSI) models via the use of a Fourier Neural Operator (FNO) in high-speed rotors supported by canned magnetic bearings (MB) immersed in supercritical carbon dioxide (SCO2). Calculating the nonlinear fluid forces in the canned MB gaps is computationally expensive due to iterative SCO₂ property evaluation and heat transfer coupling. The proposed methodology to address this issue includes the following key contributions: (1) The FNO surrogate model achieves a four-order reduction in computation time with a mean squared error of 0.01. (2) An efficient method is introduced for generating input–output image data using a 3D Reynolds-based SCO2 film model. (3) The feasibility of computing full rotordynamic and control systems, including nonlinear FSI forces, is demonstrated. (4) The models are validated against literature and are useful to predict rotordynamic instability speed in SCO2 turbomachinery.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112750"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835209","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":"Microwave vibration camera: Stereo vision assisted microwave full-field vibration visualization","authors":"Yingjie Gou ,&nbsp;Yuyong Xiong ,&nbsp;Sicheng Hong ,&nbsp;Xingjian Dong ,&nbsp;Zhike Peng","doi":"10.1016/j.ymssp.2025.112744","DOIUrl":"10.1016/j.ymssp.2025.112744","url":null,"abstract":"<div><div>The emerging field of microwave-based vibration monitoring is gaining significant attention in applications like mechanical equipment maintenance and structural health monitoring. However, the current range-angle heatmap imaging employed in full-field microwave vibrometry face substantial challenges in identifying measuring points and localizing vibration sources within complex scenarios. To this end, we introduce a unique concept: the microwave vibration camera (MVC). This novel system leverages sensor fusion by integrating the vibrational data from a microwave transceiver with spatial localization insights from a binocular camera, enabling a remarkable enhancement in vibration visualization in full field of view. In this study, we first provide a comprehensive overview of the MVC and relevant fundamental principles. Then we present a detailed methodology for the joint positioning and vibration visualization processes. Additionally, we conduct the calibration of the coordinate transformation matrix between the camera coordinate system (CCS) and the microwave antenna coordinate system (ACS) in a straightforward scenario, thoroughly evaluating its performance. Ultimately, we validate the effectiveness of the MVC through diverse scenarios and analytical dimensions, offering a desired microwave vibration visualization approach that facilitates measuring point identification and vibration source localization in complex environments.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112744"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835210","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 model updating through reliability-based sequential history matching","authors":"J. Cheng ,&nbsp;F.A. DiazDelaO ,&nbsp;P.O. Hristov","doi":"10.1016/j.ymssp.2025.112689","DOIUrl":"10.1016/j.ymssp.2025.112689","url":null,"abstract":"<div><div>Computer models enable the study of complex systems and are extensively used in fields such as physics, engineering, and biology. History Matching (HM) is a statistical calibration method that accounts for various sources of uncertainty to update model parameters and align output with observed data. By iteratively excluding regions of the parameter space unlikely to yield plausible outputs, HM identifies and samples from the so-called non-implausible domain. However, a limitation of HM is that it does not yield full Bayesian posterior distributions for model parameters. Moreover, HM requires re-execution from scratch when new data is observed, lacking the ability to leverage prior results.</div><div>To address these limitations, we propose integrating sequential Monte Carlo (SMC) methods with HM to achieve full Bayesian posterior distributions for sequential calibration. The SMC framework offers a flexible and computationally efficient means to update previously constructed distributions as new data becomes available. This approach is demonstrated using an engineering example and a cardio-respiratory case study with sequential data.</div><div>Our results show that small perturbations to the posterior distributions can be effectively learned sequentially by updating computed posterior distributions through the SMC framework, thereby enabling dynamic and efficient model updating for evolving data streams.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112689"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829519","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":"An intelligent tool wear monitoring model based on knowledge-data-driven physical-informed neural network for digital twin milling","authors":"Xiaohui Fang ,&nbsp;Qinghua Song ,&nbsp;Xiaojuan Wang ,&nbsp;Zhenyang Li ,&nbsp;Haifeng Ma ,&nbsp;Zhanqiang Liu","doi":"10.1016/j.ymssp.2025.112736","DOIUrl":"10.1016/j.ymssp.2025.112736","url":null,"abstract":"<div><div>Digital Twin provides high-precision data fusion, real-time state prediction and optimized decision support in the machining process. A reliable tool wear monitoring (TWM) model is essential to ensure data accuracy within the digital twin system and to enhance the reliability of wear state assessments. However, existing monitoring models primarily consider the wear process constraints in the average sense and do not strictly ensure compliance with the underlying physical mechanism. To address this issue, a TWM model integrating the knowledge-data-driven physical-informed neural network (PINN) is proposed in the digital twin intelligent monitoring for the milling process. On the basis of considering the monotonicity of the wear evolution process, the potential mechanism of wear rate change is employed as a hard physical constraint to construct the physical information loss. A physically significant knowledge deviation is introduced to guide the learning process of the neural network, ensuring that the model output is mapped to a value domain strictly adhering to the physical mechanism, thus improving the physical consistency. The results of milling experiments under three different machining conditions demonstrate that compared to the traditional neural network, the proposed PINN model exhibits higher precision and generalization, reducing the average <em>RMSE</em> by 11.87 %, 10.35 % and 15.04 %, respectively. In addition, after the optimal model is trained offline, the response time from online data processing to obtaining the wear output is within 2 s, while the PINN model’s computation time is on the order of milliseconds. The PINN model provides real-time tool wear data for the digital twin intelligent monitoring system with a low-latency response and integrates with the decision support system to facilitate iterative optimization of the digital twin system, enabling intelligent prediction and maintenance.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112736"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829520","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 Multi-conditional Prandtl–Ishlinskii model for nonlinearity compensation on the longitudinal-shear piezoelectric nanopositioning stage","authors":"Yixiao Yang ,&nbsp;Huazhou Kang ,&nbsp;Yunlang Xu ,&nbsp;Xiaofeng Yang ,&nbsp;Zhiping Zhang","doi":"10.1016/j.ymssp.2025.112681","DOIUrl":"10.1016/j.ymssp.2025.112681","url":null,"abstract":"<div><div>The longitudinal-shear piezoelectric nanopositioning stage (LSPNS) is a novel type of object positioning platform. It employs multi-degree-of-freedom piezoelectric stack actuators (PSAs), with LSPNS’s displacement driven by shear PSA and preload force adjusted by longitudinal PSA, rendering it highly valuable in real applications. However, during the motion of the LSPNS, the PSAs are continuously affected by various conditions such as frequency adjustment in speed regulation, preload force determined by loads, and temperature changes, which cause alteration in the dynamic nonlinear characteristics. The previous phenomenological models lack the ability to track the uncertainty of changing conditions, resulting in damage to the control accuracy of the LSPNS. In this paper, a Multi-conditional Prandtl–Ishlinskii (McPI) modeling method is proposed. It takes the advantage from material analysis to various impact factors, to build a model that combines both physics and phenomenology based on the PI model. An inverse model is then derived, and open-loop compensation for the LSPNS is ultimately achieved through feedforward control. Model fitting results demonstrate that the McPI model can accurately describe the alteration of piezoelectric nonlinear characteristics in changing conditions. Compensation results show that the average mean square error of the McPI model is decreased by 18.60% to 59.68%. Compared with other PI models, McPI model is proved to have the tracking ability to multiple conditions.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112681"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835234","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":"Time-varying damping ratio identification for structures subjected to traffic loads using signal stabilization technique","authors":"Fengzong Gong ,&nbsp;Woqin Luo ,&nbsp;Tiantao He ,&nbsp;Ye Xia","doi":"10.1016/j.ymssp.2025.112715","DOIUrl":"10.1016/j.ymssp.2025.112715","url":null,"abstract":"<div><div>The non-stationary traffic loads induce non-stationary structural responses, posing a significant challenge for the accurate identification of damping ratios. At the same time, the dynamic parameters of the vehicle-bridge system vary due to the vehicle-bridge interaction (VBI) effect. To address the issue of non-stationary structural responses in both amplitude and frequency under traffic loading, this paper proposes a signal stabilization technique to identify the time-varying damping ratio. The signal is stabilized over a short window, after which the structural parameter information is extracted by estimating the autocorrelation function. Two error suppression techniques are proposed. In combination with the time-varying autoregressive model, the damping ratio is recursively identified using Kalman filtering. The accuracy of the method was verified through numerical simulations, and the time-varying parameters of the VBI system were identified in laboratory experiments. Finally, the time-varying damping ratios of a real bridge were identified to verify the effectiveness of the method. The results demonstrate that the proposed method can identify the variation of the bridge damping ratio due to traffic loads, both in laboratory tests and analyses of real bridges. The proposed method provides a foundation for an in-depth study of damping ratios.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112715"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835211","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":"An outline of Prognostics and health management Large Model: Concepts, Paradigms, and challenges","authors":"Laifa Tao ,&nbsp;Shangyu Li ,&nbsp;Haifei Liu ,&nbsp;Qixuan Huang ,&nbsp;Liang Ma ,&nbsp;Guoao Ning ,&nbsp;Yiling Chen ,&nbsp;Yunlong Wu ,&nbsp;Bin Li ,&nbsp;Weiwei Zhang ,&nbsp;Zhengduo Zhao ,&nbsp;Wenchao Zhan ,&nbsp;Wenyan Cao ,&nbsp;Chao Wang ,&nbsp;Hongmei Liu ,&nbsp;Jian Ma ,&nbsp;Mingliang Suo ,&nbsp;Yujie Cheng ,&nbsp;Yu Ding ,&nbsp;Dengwei Song ,&nbsp;Chen Lu","doi":"10.1016/j.ymssp.2025.112683","DOIUrl":"10.1016/j.ymssp.2025.112683","url":null,"abstract":"<div><div>Prognosis and Health Management (PHM), critical for preventing unexpected failures and ensuring task completion of complex systems, is widely adopted in the fields of aviation, aerospace, manufacturing, rail transportation, energy, etc. However, PHM’s developments and applications have been seriously constrained by bottlenecks like generalization, interpretation and verification abilities. Large Model (LM), a typical and powerful representation of generative artificial intelligence (AI), heralds a technological revolution with the potential to fundamentally reshape traditional technological fields. Its strong generalization and reasoning capabilities present opportunities to address those PHM’s bottlenecks existing. To this end, by systematically analyzing the current challenges and bottlenecks in PHM, as well as the advantages of Large Model, we propose a novel concept and corresponding three typical paradigms of PHM Large Model (PHM-LM) by the combination of the Large Model with PHM. Additionally, couples of feasible technical approaches for PHM-LM within the framework of the three paradigms are provided to address core issues confronting PHM and to bolster PHM’s core capabilities. Moreover, a series of technical challenges throughout the entire construction and application process of PHM-LM have been deeply discussed for further research recommendation. The comprehensive effort herein offers a comprehensive PHM-LM technical framework, and provides avenues for new methodologies, new technologies, new tools, new platforms and applications of PHM, which also potentially innovates design mode, research &amp; development mode, verification and application mode of PHM, i.e., from traditional customization to generalization, from discriminative approaches to generative methods, and from idealized conditions to practical applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112683"},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825508","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":"Investigation of impact and vibration behaviours of composite honeycomb sandwich shell panels with foam reinforcement","authors":"Zelin Li ,&nbsp;Hui Li ,&nbsp;Yao Yang ,&nbsp;Chaohui Ren ,&nbsp;Haiyang Zhang ,&nbsp;Haijun Wang ,&nbsp;Jin Zhou ,&nbsp;Bo Zhou ,&nbsp;Zhongwei Guan","doi":"10.1016/j.ymssp.2025.112676","DOIUrl":"10.1016/j.ymssp.2025.112676","url":null,"abstract":"<div><div>The impact and vibration behaviours of composite honeycomb sandwich shell panels with foam reinforcement (RF-CHSSPs) are researched analytically and experimentally. Initially, a dynamic model of the RF-CHSSPs is created to predict the vibration and impact characteristics, with the equivalent Poisson’s ratio and elastic modulus of the core are determined to consider the effect of the ratio of honeycomb cells and foam. Time-domain minimum residual technique and Broyden iterative method are used to solve the natural frequency and resonant response by using von Karman’s theory and the high-order shear deformation shell principle. Also, based on the quasi-static method and modified failure criteria, the curves of impact displacement–time, load-time and load–displacement are plotted. Meanwhile, the low-velocity impact and base vibration excitation experiments are carried out on the prepared RF-CHSSP specimens to verify the accuracy of the established model and the vibration and impact suppression ability of foam on the composite honeycomb sandwich shell panels. The results show that the maximum calculated errors of impact displacement, load, natural frequency and resonance response are 5.1, 5.8, 4.7 and 9.0%, respectively. Moreover, for the specimens without foam reinforcement, the impact contact force of the RF-CHSSP specimens is improved by 13.2%, and the impact displacement and resonance response are reduced by 15.8 and 96.6%, respectively. The manufacturing technology, approach to problem resolution and valuable discoveries of current study show the way forward for the creation and application of such sophisticated shells.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828599","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}