Mechanical Systems and Signal Processing最新文献

{"title":"3D-DIC full field experimental modal analysis of a demo airplane by using low-speed cameras and a reconstruction approach","authors":"Davide Mastrodicasa ,&nbsp;Emilio Di Lorenzo ,&nbsp;Simone Manzato ,&nbsp;Bart Peeters ,&nbsp;Patrick Guillaume","doi":"10.1016/j.ymssp.2025.112387","DOIUrl":"10.1016/j.ymssp.2025.112387","url":null,"abstract":"<div><div>Experimental Modal Analysis (EMA) has developed into a major technology for the study of structural dynamics in the past several decades. Through Experimental Modal Analysis, complex structure phenomena in structural dynamics can be represented using decoupled modes consisting of natural frequency, modal damping, and mode shapes. The dynamic properties of structures can be extracted from both forced and ambient vibration tests. Whether the object is a wind turbine blade rotating at a certain speed, a bridge sustaining traffic, or an airplane under wind excitation, Modal Analysis can be applied to provide insightful solutions. These tests are mainly performed using point-wise sensors connected to the structure. A limited number of transducers might not be able to comprehensively measure the dynamic response, especially when dealing with large-size or very small structures, lightweight components, or rotating structures. This is one of the reasons behind the development of image processing techniques, like Digital Image Correlation (DIC), to perform modal analysis. A particular field of interest in using DIC for vibration analysis is in using cheap, light, and low-speed cameras to detect a structure’s high-frequency behavior. Nevertheless, except for a few highly specialized and controlled scenarios, the effectiveness of camera-based EMA is constrained by the relatively low sampling frequency of cameras in contrast to accelerometers, strain gauges, and laser Doppler vibrometers. In this paper, we introduce an innovative acquisition method designed to estimate modal parameters beyond the Nyquist–Shannon limit (i.e., half of the camera’s frame rate). This is achieved through the utilization of periodic excitation and signal reconstruction techniques. As a result, it becomes feasible to reconstruct a high-sampled displacement signal using low-speed cameras. The accuracy of the methodology is numerically investigated by using a simple MDOFs system as a proof of concept. Furthermore, an experimental validation on a simple airplane mock-up is presented. The displacements are obtained using a stereo camera setup and then computed by DIC. Finally, they are combined with the force signal to compute the structure’s FRFs for the modal parameter estimation. Furthermore, the DIC estimated modal parameters are validated by using accelerometers mounted on the test structure, and a full field validation of the corresponding numerical model is presented.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112387"},"PeriodicalIF":7.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077534","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 new fully decoupled strategy for reliability optimization based on adaptive Kriging with improved efficient global optimization and weighted K-means clustering","authors":"Haizheng Song ,&nbsp;Huagang Lin ,&nbsp;Changcong Zhou ,&nbsp;Lei Li ,&nbsp;Zhufeng Yue","doi":"10.1016/j.ymssp.2025.112402","DOIUrl":"10.1016/j.ymssp.2025.112402","url":null,"abstract":"<div><div>Reliability-based design optimization (RBDO) is regarded as a more systematic approach to structural design that seamlessly combines reliability and design for optimization. However, the computational cost will be often substantial due to necessarily demanding reliability analysis during the optimization design procedure. This paper proposes a novel decoupling approach to effectively solve the RBDO problem. Based on the augmented reliability theory and Bayes’ rule, the failure probability function (FPF) is estimated through establishing a new adaptive Kriging model with an improved efficient global optimization (IEGO) and weighted K-means clustering (WKC). The WKC algorithm can effectively mitigate the clustering effect of the added sample points by partitioning original sample set into <em>K</em> clusters. Combined with IEGO, the sample points will be selected within each cluster that contribute the most to improving the Kriging model. This approach ensures the efficiency and accuracy of the adaptive Kriging model in estimating the FPF. The RBDO problem in which the probabilistic constraints are substituted using the estimated FPF can be fully decoupled into a deterministic optimization problem, and demonstrated as an enabling efficient solution. Thus, it is worth noting that the primary computational cost associated with the decoupling process arises from estimating the FPF. In this paper, the computational cost of solving the RBDO is significantly reduced by developing an adaptive Kriging model capable of estimating the FPF accurately and efficiently. One numerical example and three practical engineering applications are employed to demonstrate the accuracy and efficiency of the proposed method compared to other methods.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112402"},"PeriodicalIF":7.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077533","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":"Enhanced representation of the nonlinear dynamic characteristics of ball screw feed drive system through developing a three-state model","authors":"Min Wan ,&nbsp;Jia Dai ,&nbsp;Hui Tian ,&nbsp;Xue-Bin Qin ,&nbsp;Wei-Hong Zhang","doi":"10.1016/j.ymssp.2025.112371","DOIUrl":"10.1016/j.ymssp.2025.112371","url":null,"abstract":"<div><div>This paper develops a three-state model to represent the nonlinear dynamic characteristics of the ball screw feed drive system (BSFDS), including the impact of backlash during velocity reversal. The three states are distinguished as a rigid model during unidirectional motion, a rigid model capturing the backlash phenomenon during velocity reversal, and a flexible model capable of describing the torsional vibrations. Impact of backlash on friction and inertia is considered in the three-state model, with a special friction model established to depict the effect of backlash. To identify the parameters involved in the model, a recursive maximum likelihood (RML) algorithm is proposed, based on the distinct signals designed specially for exciting the three states. The accuracy of parameter identification is enhanced through the combined use of iterative learning (IL). Based on the identified friction results using the IL-based RML, the model parameters are finally determined through least squares fitting. Experiments and comparisons are conducted to verify the effectiveness of this work.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112371"},"PeriodicalIF":7.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077552","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":"Identification of aeroacoustic dipole sources of subsonic fan with active acoustic intensity method","authors":"Yijun Mao ,&nbsp;Xiaojiang Gu ,&nbsp;Chen Xu ,&nbsp;Le Zhou","doi":"10.1016/j.ymssp.2025.112391","DOIUrl":"10.1016/j.ymssp.2025.112391","url":null,"abstract":"<div><div>Various methods have been developed to identify turbomachinery sources based on experimental measurements and signal processing techniques. In the design phase of turbomachinery, aeroacoustic source identification method based on flow simulation is also useful to guide the aeroacoustic design optimization. Wall pressure fluctuation is usually used as the criterion to identify the aeroacoustic sources of turbomachinery operating at subsonic flow, but it ignores the effect of source motion on the acoustic radiation capability. This paper presents a source identification method based on active acoustic intensity to identify the location and strength of dipole sources on both stationary and moving solid boundaries. This method is applied to investigate the aeroacoustic dipole sources of a low-speed mixed-flow fan. Furthermore, the tonal and broadband components of the noise sources are accurately identified by combining the developed source identification method with a pressure triple decomposition method.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112391"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077556","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 unified approach for time-domain and frequency-domain finite element model updating","authors":"Dan Li ,&nbsp;Jiajun Zhou ,&nbsp;Xinhao He","doi":"10.1016/j.ymssp.2025.112361","DOIUrl":"10.1016/j.ymssp.2025.112361","url":null,"abstract":"<div><div>Reliable finite element (FE) models play a vital role in accurately predicting structural behaviors under various loading conditions in structural engineering applications. This paper presents a unified approach for solving time-domain and frequency-domain FE model updating problems. In this approach, both types of problems are formulated as stochastic dynamic systems with embedded parameter-to-data maps, enabling the estimation of unknown model parameters. The unscented Kalman filter (UKF) is employed as an effective tool to solve these dynamic systems and update the parameters in a derivative-free manner. Additionally, this study addresses specific aspects of FE model updating, including constraint implementation, covariance inflation, and sparse regularization. The analytical solutions for the Kalman gain and updated parameters under bound constraints are derived, guaranteeing that the model parameters adhere to predefined bounds. A method for inflating the estimated error covariance is used to mitigate issues caused by abrupt fluctuations in the measured structure. Covariance inflation techniques are applied to account for uncertainties not accurately captured by assumed covariance matrices. Furthermore, a variable transformation strategy is adopted to convert the sparse regularization problem into a Tikhonov regularization problem, which can be solved by the UKF with measurement augmentation. Sparse regularization facilitates more accurate and interpretable results in applications such as damage identification. The proposed unified approach is verified through extensive validation examples. The results demonstrate the effectiveness and reliability of the approach in accurately estimating the unknown parameters of FE models for structural engineering applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112361"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic machine learning pipeline using topological descriptors for real-time state estimation of high-rate dynamic systems","authors":"Yang Kang Chua ,&nbsp;Daniel Coble ,&nbsp;Arman Razmarashooli ,&nbsp;Steve Paul ,&nbsp;Daniel A. Salazar Martinez ,&nbsp;Chao Hu ,&nbsp;Austin R.J. Downey ,&nbsp;Simon Laflamme","doi":"10.1016/j.ymssp.2025.112319","DOIUrl":"10.1016/j.ymssp.2025.112319","url":null,"abstract":"<div><div>High-rate systems are structures that undergo rapid changes, exhibiting dynamics that evolve over short durations, often less than 100 ms. In this study, we propose a probabilistic machine learning pipeline for estimating the state of a high-rate system. Our approach begins with the extraction of features using topological data analysis (TDA) that capture the underlying structure of datasets. We examine the design of probabilistic models for structural state estimation, emphasizing the importance of prediction intervals. Our method validation involves two datasets: a toy example of linear chirp signals and an experimental dataset from the Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research (DROPBEAR) testbed. We use metrics such as mean absolute error (MAE) and time response assurance criterion (TRAC), along with uncertainty metrics such as negative log-likelihood (NLL), calibration curves, and expected calibration error (ECE), to evaluate model performance. The results indicate that the RNN–NNE model achieves the lowest MAE of 5.705 mm, the highest TRAC, and the lowest ECE of 7.335%, highlighting its superior predictive accuracy and robustness in handling uncertainty.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112319"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077558","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 uncertainty quantification and accuracy enhancement method for deep regression prediction scenarios","authors":"Teng Zhang ,&nbsp;Fangyu Peng ,&nbsp;Rong Yan ,&nbsp;Xiaowei Tang ,&nbsp;Jiangmiao Yuan ,&nbsp;Runpeng Deng","doi":"10.1016/j.ymssp.2025.112394","DOIUrl":"10.1016/j.ymssp.2025.112394","url":null,"abstract":"<div><div>Accurate regression prediction is a critical objective in industry; however, epistemic and aleatoric uncertainties can significantly impact prediction accuracy. Existing research primarily focuses on either prediction or uncertainty estimation, with limited studies addressing joint prediction methods and accuracy enhancement. This paper proposes an uncertainty quantification and accuracy enhancement method, referred to as <em>UQAE</em>, for deep regression prediction scenarios. This approach enables the simultaneous provision of both point predictions and uncertainty estimation results for any deep regression task. Furthermore, it facilitates a quadratic improvement in point prediction accuracy. Specifically, threshold monitoring and parameter-sharing asynchronous training strategies are implemented to ensure the joint prediction of point estimates and distribution-free intervals. Fuzzy rules are incorporated to enhance the accuracy of point predictions, providing interpretability based on the joint predictions. The proposed method is rigorously compared and evaluated using nine generalized manufacturing-related datasets, demonstrating significant improvements in both point prediction accuracy and uncertainty prediction interval estimation. Additionally, this approach is expected to advance regression prediction research in manufacturing, promoting higher accuracy and interpretability. The <em>UQAE</em> method and the associated datasets are available at <span><span><em>https://github.com/ZhangTeng-Hust/UQAE</em></span><svg><path></path></svg></span><strong><em>.</em></strong></div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112394"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077553","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-data joint identification of rolling mill parameters and multi-frequency external disturbance excitation","authors":"Ming Wang ,&nbsp;Xiaoyan Xiong ,&nbsp;Huidong Xu ,&nbsp;Xiaofeng Qin ,&nbsp;Dongping He ,&nbsp;Xiangrong Wang ,&nbsp;Tao Wang","doi":"10.1016/j.ymssp.2025.112389","DOIUrl":"10.1016/j.ymssp.2025.112389","url":null,"abstract":"<div><div>Complex nonlinear factors exist in the rolling interface of the mill, resulting in a rich nonlinear dynamic behavior of the roll system. Accurate dynamic model and parameters are the prerequisite for correctly analyzing the vibration characteristics of the roll system. In this paper, the influence of roll vibration on the friction state of rolling deformation zone is considered, and the dynamic rolling force model under the influence of variable friction factors is established. The vertical-horizontal coupling nonlinear dynamic model of the roll system under the action of dynamic rolling force is further established for the electric press-down mill, in which the tilting of the press-down stud caused by vibration is taken into account. The roll vibration acceleration signals are collected through experiments, and the equivalent stiffness, equivalent damping, dynamic change of rolling force and external disturbance force in the model are identified by extended Kalman filtering algorithm. The identification results are verified by the experimental data of dynamic rolling force. Based on the identification results, the 6th order sinusoidal approximation functions of the external disturbance forces are established. The effects of parameters on the amplitude-frequency characteristics of 2nd super-harmonic resonance, 1/2 sub-harmonic resonance, and the combined resonance of the roll under multi-frequency excitation are analyzed. The findings of this study can provide theoretical guidance for mill parameters design and rolling process selection to improve the stability of the mill.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112389"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077554","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":"Contact dynamic modeling and corner contact analysis of the spur gear pair with pitch deviations","authors":"Yuankui Luo ,&nbsp;Lixin Xu","doi":"10.1016/j.ymssp.2025.112377","DOIUrl":"10.1016/j.ymssp.2025.112377","url":null,"abstract":"<div><div>Pitch deviations have a significant effect on the contact state, transmission accuracy and running stability of a gear pair. In this work, a contact dynamic model of a spur gear pair is established on the basis of the pitch deviations. This model consists of three parts: a geometric mathematical model, a time-varying mesh stiffness (TVMS) model, and a dynamic differential equation and contact model. In the first part, mathematical equations are used to represent the tooth profiles of the gear pair with pitch deviations, which lays a foundation for the subsequent assessment of contact. In the second part, considering pitch deviations and the accurate root transition curve, the tooth is simplified as a cantilever beam on the root circle, and the TVMS of the gear pair is deduced using the potential energy method. In the third part, the contact modes of the gear pair with pitch deviations are analyzed, which shows that corner contact may occur during meshing. A mathematical contact judgment method is then proposed to determine eight meshing points (normal meshing points and corner contact meshing points) that may arise in the meshing state. Finally, dynamic differential equations of the gear pair are established. On the basis of the proposed model, effects of different pitch deviations and the random pitch deviations upon the vibration acceleration and the dynamic transmission error (DTE) of the gear pair are studied. The accuracy of the proposed contact dynamic model of the gear pair with pitch deviations is verified by way of an experiment reported in the literature and self-test data. The proposed model and research results lay a foundation for research into performance analysis, design optimization and precision manufacturing.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112377"},"PeriodicalIF":7.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077555","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":"Sparse-assisted blade tip timing signal decomposition and automatic resonance region identification method","authors":"Daitong Wei ,&nbsp;Tao Yu ,&nbsp;Peixin Gao ,&nbsp;Hongkun Li ,&nbsp;Yugang Chen","doi":"10.1016/j.ymssp.2025.112390","DOIUrl":"10.1016/j.ymssp.2025.112390","url":null,"abstract":"<div><div>Rotating blades are affected by various factors such as rotor–stator interaction, rotor vibration, unstable fluid excitation, and foreign object damage during operation, these factors can induce synchronous and asynchronous vibrations. Typically, the vibration states require offline analysis and diagnosis by experienced engineers. To achieve online automatic identification of synchronous and asynchronous resonance states of rotating blades, a blade tip timing (BTT) signal decomposition method based on sparse auxiliary optimization is proposed, which extracts the synchronous and asynchronous resonance signal components. In addition, automatic identification methods for synchronous and asynchronous resonance regions based on short-time energy (STE) and short-time zero-crossing rate (STZCR) are proposed separately. Finally, the effects of optimization parameters, transient response and vibration coupling effect on vibration signal decomposition and resonance region identification are revealed by means of numerical simulation and experimental verification. This study provides an effective approach for the online analysis of BTT signals and the automatic identification of resonance regions, especially provides effective theoretical support for blade fault diagnosis based on asynchronous vibration signal.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112390"},"PeriodicalIF":7.9,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035343","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}