Mechanical Systems and Signal Processing最新文献

{"title":"Enhanced matrix completion technique for blade tip timing signal","authors":"Jiahui Cao ,&nbsp;Zhibo Yang ,&nbsp;Hongfei Zu ,&nbsp;Bo Yan ,&nbsp;Xuefeng Chen","doi":"10.1016/j.ymssp.2025.112565","DOIUrl":"10.1016/j.ymssp.2025.112565","url":null,"abstract":"<div><div>Blade tip timing (BTT) is a potential non-contact vibration measurement for rotating blades. Identifying characteristic parameters or recovering the (power) spectrum of vibrations for condition monitoring from BTT data is a critical issue in the actual application. However, due to the measurement principle and installation restrictions, BTT signal is severely undersampled and then is hard to be analyzed by traditional signal processing methods. To clear the obstacle caused by undersampling on the application of BTT, we proposed an enhanced matrix completion technique (EMCT) for BTT signal post-processing. EMCT contains two procedures: covariance (matrix) reconstruction and followed by parameter estimations. First, based on the finding that the covariance matrix of BTT data is a low-rank and symmetric positive semidefinite Toeplitz matrix, we develop a matrix completion algorithm to reconstruct covariance. Then, based on the reconstructed covariance matrix, we extract frequency and amplitude/power parameters using root-MUSIC and least square algorithms. Due to dual structural prior, EMCT performs better than covariance-based methods relying on a single prior in estimation accuracy and precision. More importantly, EMCT also shows potential in reducing the number of probes. In addition, due to its gridless nature, EMCT is free from the basis mismatch issue and can achieve continuous parameter estimation. Finally, the effectiveness of EMCT has been repeatedly validated by both simulations and experiments.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112565"},"PeriodicalIF":7.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643416","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-assisted disentanglement framework for rolling bearing fault diagnosis under time-varying speed conditions","authors":"Yuhui Xu,&nbsp;Yimin Jiang,&nbsp;Tangbin Xia,&nbsp;Dong Wang,&nbsp;Zhen Chen,&nbsp;Ershun Pan,&nbsp;Lifeng Xi","doi":"10.1016/j.ymssp.2025.112588","DOIUrl":"10.1016/j.ymssp.2025.112588","url":null,"abstract":"<div><div>Rolling bearings of rotating machines are frequently required to operate under time-varying speed conditions. Although numerous deep learning methods have been advanced for fault diagnosis of rolling bearings, most are anchored on the assumption of constant speed or a few speed conditions. Extracting underlying fault-related information without interference caused by continuous speed changes is still problematic. To this end, a dynamic model-assisted disentanglement (DMAD) framework is proposed, enhancing the adaptability to time-varying speed conditions by a representation disentanglement technique with dynamic model simulations assisted in network training. Firstly, a four-degree-of-freedom dynamic model of rolling bearings considering speed variations is established to provide augmented training data with diverse health and speed conditions. Furthermore, a directed representation disentanglement network based on adversarial learning is developed to separate deep representations of health conditions and rotational speeds. Due to the divergences between simulated and real data, a contrastive model calibration method is also proposed to calibrate the network trained with simulated data, thus facilitating the generalization performance of fault diagnosis. Experiments conducted on two experimental datasets and a factory case demonstrate the superiority of the proposed DMAD framework, which provides reliable rolling bearing fault diagnosis under time-varying speed conditions.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112588"},"PeriodicalIF":7.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675614","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 tunable electromagnetic stiffness with bistable, hardening and softening characteristics","authors":"Maksymilian Bednarek ,&nbsp;Bipin Balaram ,&nbsp;Jan Awrejcewicz","doi":"10.1016/j.ymssp.2025.112577","DOIUrl":"10.1016/j.ymssp.2025.112577","url":null,"abstract":"<div><div>Nonlinear stiffness elements have acquired wide application in recent years to augment the performance of systems like vibration absorbers, vibration isolators and energy harvesters. Hardening, softening, quasi-zero, bistable and multi-stable stiffness characteristics have been shown to improve system performance in a variety of contexts. Still, the main challenge to the wide use of nonlinear stiffness remains the difficulty in physically realising stiffness mechanisms with the desired load–displacement relationship. Even though a wide variety of physical mechanisms have been proposed, they typically have the disadvantage that in order to change the force amplitude value or the character of load–displacement curve, one or more components of the mechanism have to be changed by dismantling the assembly. Electromagnetic stiffness mechanisms make it easier to tune the force amplitude but are usually limited to a single load–displacement curve. The present article proposes an electromagnetic stiffness mechanism, based on a particular arrangement of permanent magnet and current carrying coil, which can be tuned very easily by varying the polarity and value of current through the coil. Just by varying the current through the coil, the proposed mechanism exhibits linear, hardening, softening and bistable stiffness characteristics. The construction of the mechanism is detailed and different stiffness properties are experimentally demonstrated. A closed form expression for stiffness force, which is in very good agreement with experimental curve, is arrived at, with magnet-coil parameters and current as variables. Analytical expression for threshold current values at which softening to hardening and bistable transition happens are obtained and experimentally validated. The method of multiple scales is used to arrive at an asymptotic solution of the oscillator with the proposed electromagnetic stiffness. This solution is also shown to be in excellent agreement with experimental values. A numerical study of dynamic properties are also carried out and experimentally validated.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112577"},"PeriodicalIF":7.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642957","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":"Acoustic-structure interaction-based identification for subsurface voids in steel-concrete composite structure: Experimental study and numerical simulation","authors":"Shiyu Gan ,&nbsp;Xin Nie ,&nbsp;Hongbing Chen ,&nbsp;Yuanyuan Li","doi":"10.1016/j.ymssp.2025.112595","DOIUrl":"10.1016/j.ymssp.2025.112595","url":null,"abstract":"<div><div>Steel-concrete composite structure (SCCS) has gained wide application in infrastructures for its excellent mechanical properties and reasonable economy, the structural integrity and service performance of which, however, are threatened by interfacial defects between steel and concrete. The detection methods focusing on vibration characteristics have demonstrated preferable effectiveness in detecting subsurface voids in SCCS compared to other non-destructive testing methods. This study provides a comprehensive understanding of the impact response (IR) method for detection in this regard, integrating theoretical analysis, experimental study, and multi-physics coupled numerical simulation with particular emphasis on considering acoustic-structure interaction. This coupling effect is confirmed to correlate with three-dimensional sizes of void defects, facilitating the identification of subsurface voids. A specimen is specially designed and subjected to IR tests to furnish results for experimental validation. The simulation results reveal frequency-splitting phenomena and distinct distributions of acoustic fields inside voids, which are attributed to acoustic-structure interaction. Noteworthy influences of void depth on the vibration characteristics of the structure are also highlighted, including apparent frequency deviation and redistribution of damping effect in modal analysis, as well as beat phenomena in transient analysis. Moreover, the identification of subsurface voids along with their depth is attained in practice by the proposed data analysis strategy based on attenuation characteristics of vibration response and waveform fitting. The findings of this study also underscore the significance of considering acoustic-structure interaction when analyzing vibration characteristics during interfacial defect detection in SCCS.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112595"},"PeriodicalIF":7.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643413","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":"S-WhiteSVDD: A feature fusion approach for diesel engine performance degradation assessment using Multi-Source impulse signals","authors":"Anzheng Huang ,&nbsp;Zhiwei Mao ,&nbsp;Fengchun Liu ,&nbsp;Xiangxin Kong ,&nbsp;Shenxiao Chen ,&nbsp;Jinjie Zhang ,&nbsp;Zhinong Jiang","doi":"10.1016/j.ymssp.2025.112589","DOIUrl":"10.1016/j.ymssp.2025.112589","url":null,"abstract":"<div><div>Performance degradation assessment (PDA) is a critical component of predictive health management (PHM). The mixed multi-source impulse characteristics of diesel engine vibration signals make PDA more challenging compared to rotating machinery. To address the unique characteristics of diesel engine signals, this study proposes a Subspace-Whitening Support Vector Data Description (S-WhiteSVDD) feature fusion approach that combines knowledge-based features with deep learning features. The method tracks cross-cycle variations of multiple homologous impulses and constructs a feature subspace for each impulse. Whitening transformation ensures balanced stretching and compression of subspace data across all components, preventing features with large variances from dominating the decision boundary. This approach aligns more closely with the data manifold and enables precise control of anomaly boundaries. To overcome the challenge of significant health indicator (HI) fluctuations that hinder early fault detection, the method integrates the interpretability of knowledge-based features with the complex mapping capabilities of deep features. This fusion enhances the richness of feature representation, facilitating the detection of early fault onset. The effectiveness and superiority of the proposed method are demonstrated through both valve degradation simulations and nozzle degradation engineering case studies. The constructed HI effectively indicates component degradation. The proposed approach shows strong potential for practical engineering applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112589"},"PeriodicalIF":7.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642632","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":"Improved deep transfer learning and transmission error based method for gearbox fault diagnosis with limited test samples","authors":"Hongbo Yang ,&nbsp;Zeyu Wang ,&nbsp;Miao Xu ,&nbsp;Dongpo Yang ,&nbsp;Zhifen Zhao","doi":"10.1016/j.ymssp.2025.112593","DOIUrl":"10.1016/j.ymssp.2025.112593","url":null,"abstract":"<div><div>As an important component of mechanical transmission system, gearbox state is critical to system safety and efficiency. The fault diagnosis of gearbox is of great significance for monitoring its operation states and identifying potential problems. Firstly, to improve the generalization ability of traditional fault diagnosis model and reduce the diagnostic loss for similar faults occurred in different conditions, an improved deep transfer learning network model is established based on deep subdomain adaptation method and residual feature extraction network. Then, taking a heavy commercial vehicle gearbox as research object, a dynamic simulation model considering its fault state is established, and the transmission error bench test is designed to verify the correctness of the model with different load torque. Finally, under the condition of limited test samples, a gearbox fault diagnosis method based on improved network model and simulation data is proposed and its effectiveness is verified through different comparative experimental tasks and evaluation metrics. The results show that, by using dynamic simulation data of gearbox transmission error, the established deep transfer learning model and proposed gearbox fault diagnosis method can obtain excellent diagnostic performance with high diagnosis precision and low training loss, and excessive test resource investment can be avoided effectively.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112593"},"PeriodicalIF":7.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643414","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":"Evaluation of the first-passage probability of non-stationary non-Gaussian structural responses with linear moments and copulas","authors":"Chao-Huang Cai ,&nbsp;Li-Zhong Jiang ,&nbsp;Zhao-Hui Lu ,&nbsp;Yu Leng ,&nbsp;Chun-Qing Li","doi":"10.1016/j.ymssp.2025.112553","DOIUrl":"10.1016/j.ymssp.2025.112553","url":null,"abstract":"<div><div>The evaluation of the first-passage probability of non-stationary non-Gaussian structural responses remains a great challenge in the field of random vibrations. In the present paper, a novel method is proposed for evaluating this first-passage probability, whose main contribution is to construct the joint probability density function (PDF) of the structural response and its derivative process under the consideration of their non-Gaussianities and nonlinear correlations. Cubic polynomial models of Gaussian process are developed to characterize the non-Gaussianities of the structural response and its derivative process, whose polynomial coefficients at each instant time are explicitly determined from their corresponding first four linear moments. These linear moments are accurately evaluated using a proposed method combining Sobol sequence with polynomial smoothing. The marginal PDFs and cumulative distribution functions (CDFs) of the structural response and its derivative process are then derived from these polynomial models. Based on Akaike information criterion (AIC) and the marginal CDFs, the optimal copula function at each instant time is selected to capture the linear/nonlinear correlation between the structural response and its derivative process. And thus, the joint PDF is constructed and the first-passage probability is evaluated. The applicability of the proposed method is validated by several numerical examples. It can be concluded that the proposed method provides satisfactory results in evaluating the linear moments, fitting probability distributions, and estimating the first-passage probabilities of non-stationary non-Gaussian structural responses.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112553"},"PeriodicalIF":7.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636709","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":"Joint load-parameter-response identification using a physics-encoded neural network","authors":"Lanxin Luo ,&nbsp;Limin Sun ,&nbsp;Mingming Song ,&nbsp;Jiaxin Liu ,&nbsp;Yixian Li ,&nbsp;Yong Xia","doi":"10.1016/j.ymssp.2025.112597","DOIUrl":"10.1016/j.ymssp.2025.112597","url":null,"abstract":"<div><div>The joint identification of the input-parameter-output from sparse measurements is essential to evaluate the safety condition of civil infrastructures. Existing physics-based methods require that the loading locations and covariances of process and observation noises are available. In contrast, pure data-driven approaches are limited in their generalization ability and interpretability. To address these issues, a physics-data-driven method is proposed for joint load-parameter-response identification. It consists of a data-driven and two physics-based modules. The former is a convolutional residual autoencoder (RAE) for load identification. In the physical modules, the finite element method is used for model updating, and the Newmark-beta algorithm is embedded to solve the structural dynamics. They are encoded into a deep learning architecture to realize the forward response calculation and backward system identification. Subsequently, the difference between the predicted and measured responses is formulated as the loss function, by minimizing which the RAE is trained, and the structural parameters are identified simultaneously. In such a process, structural property and load time history are learned in a novel self-supervised manner using output data only. The proposed method is finally applied to a numerical two-span beam and a laboratory-tested cantilever beam. Results show that the proposed approach can identify the structural load, parameters, damage, and responses accurately. The effect of observation noise, sensor placement, and load conditions on the identification results are discussed.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112597"},"PeriodicalIF":7.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642629","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 simulation and experimental study of transfer learning for mode detection of aeroengine fan noise","authors":"Sicong Liang ,&nbsp;Wenjun Yu ,&nbsp;Xun Huang","doi":"10.1016/j.ymssp.2025.112555","DOIUrl":"10.1016/j.ymssp.2025.112555","url":null,"abstract":"<div><div>The growing popularity of deep learning can be attributed to its capacity to model intricate nonlinear problems. However, this approach is constrained by its reliance on extensive datasets, making it less applicable in certain real-world situations. In the context of aeroengine applications, we propose a multi-constraint transfer learning framework that employs pressure measurements outside the turbofan duct to identify associated fan noise modes. We validate this methodology through both simulations and practical experiments. Traditional transfer learning methods apply knowledge derived from a single source task but face challenges due to factors like measurement location discrepancies and varying duct geometries. To mitigate these limitations, we establish a dataset comprising multiple source tasks, enabling our framework to focus on transfer learning with these diverse inputs. We contrast our method with traditional transfer learning across three scenarios: distinct measurement locations and heterogeneous geometries using the simulations, and the experimental tests. The results underscore the benefits of employing multiple sources in enhancing the accuracy and robustness of our deep learning model.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112555"},"PeriodicalIF":7.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636707","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-order synchrosqueezing transform leveraging informative harmonics selection for instantaneous angular speed estimation","authors":"Fadi Karkafi ,&nbsp;Quentin Leclère ,&nbsp;Jérôme Antoni ,&nbsp;Dany Abboud ,&nbsp;Mohammed El Badaoui","doi":"10.1016/j.ymssp.2025.112567","DOIUrl":"10.1016/j.ymssp.2025.112567","url":null,"abstract":"<div><div>Under nonstationary conditions, estimating the instantaneous angular speed (IAS) of rotating machines from vibration measurements is a practical way for encoder-free condition monitoring. Existing IAS estimation methods typically proceed from two main approaches: phase demodulation and time–frequency representation (TFR). However, several methods from these two categories often face challenges such as a single-harmonic focus and difficulty in distinguishing between clean and noisy harmonics. Noisy harmonics are characterized by low signal-to-noise ratio, energy attenuation, and interference from other components. In contrast, clean harmonics, revolve around the actual IAS, thus improving the accuracy of its estimation. This paper introduces a novel approach, the Multi Order Synchrosqueezing Transform (MOST), which stands out due to its ability to automatically identify and utilize informative harmonics, setting it apart from traditional methodologies. MOST aims at providing a more accurate IAS estimation under nonstationary conditions. It proceeds from an initial IAS estimate from a synchrosqueezing transform and then introduces a normalized threshold aimed at preserving the energy of harmonics in the TFR at each time bin. Next, it constructs a probability density function using multiple harmonics. By doing so, it retains essential information from selected harmonics while effectively filtering out irrelevant noise. To emphasize the effectiveness of this approach, the paper conducts an exhaustive investigation. The evaluation includes rigorous performance testing across multiple noise levels to demonstrate its robustness, alongside assessments based on simulated data and two benchmark experimental datasets. This shows the impaction of MOST in its generalizability to various rotating machinery types and vibration signals, highlighting its potential for broader industrial applications.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"230 ","pages":"Article 112567"},"PeriodicalIF":7.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636708","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}