Mechanical Systems and Signal Processing最新文献

{"title":"A frequency-domain sequential Bayesian filter for sparse and broadband force estimation problems","authors":"M. Aucejo,&nbsp;O. De Smet","doi":"10.1016/j.ymssp.2025.112729","DOIUrl":"10.1016/j.ymssp.2025.112729","url":null,"abstract":"<div><div>This paper presents a novel method for estimating the external sources acting on a mechanical structure in the frequency domain. Under the assumption of spatially sparse and broadband sources, a sequential Bayesian filter is derived. Its general structure follows that of a sequential Kalman-like filter, which is commonly used for input-state estimation problems in the time domain. This paper also includes an original Bayesian method for computing the noise variances of each measurement channel, which is a key element for the proper tuning of the proposed filtering algorithm. The proposed method is validated by a numerical experiment and an experimental application. The numerical experiment considers a simply supported beam subjected to a broadband point force under different operating conditions, while the experimental application deals with the identification of a point force acting on a simply supported plate. The comparison made with approaches available in the literature shows that the proposed strategy is able to estimate the external forces acting on a mechanical structure with the best trade-off between computational time/resources and accuracy.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112729"},"PeriodicalIF":7.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838765","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 novel lightweight DDPM-based data augmentation method for rotating machinery fault diagnosis with small sample","authors":"Caizi Fan ,&nbsp;Yongchao Zhang ,&nbsp;Hui Ma ,&nbsp;Kun Yu ,&nbsp;Zeyu Ma","doi":"10.1016/j.ymssp.2025.112741","DOIUrl":"10.1016/j.ymssp.2025.112741","url":null,"abstract":"<div><div>Efficient and intelligent fault diagnosis of rotating machinery is crucial for ensuring the safety and reliability of industrial systems. In practical engineering, data collected from mechanical equipment is often scarce, making accurate fault identification under small sample conditions challenging. Denoising diffusion probability model (DDPM), as a new paradigm of generative models, is widely used for data generation. However, DDPM involves multiple reverse diffusion steps in the generation process, which requires substantial computational resources. To address this issue, a novel lightweight DDPM is proposed for generating fault signals in limited sample scenarios. This method incorporates multi-dconv head transposed attention (MHTA) into the U-Net architecture, shifting attention calculations from the pixel dimension to the channel dimension by combining point-wise and depth-wise convolution, thereby significantly reducing computational complexity. Meanwhile, this method enables the model to capture global context information while also focusing on local details, enhancing the model’s representational capability. Additionally, a composite indicator is developed to evaluate the quality of the synthesized signals across multiple feature dimensions. The effectiveness of the proposed MHTA-DDPM is validated through three cases: simulated signals, real gear and bearing fault signals. The results indicate that the proposed model can generate high-quality and diverse fault signals with good generalization capability, improving fault diagnosis accuracy even under limited sample conditions.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112741"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838760","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":"Fault-relevance-based, multi-sensor information integration framework for fault diagnosis of rotating machineries","authors":"Sungjong Kim ,&nbsp;Seungyun Lee ,&nbsp;Jiwon Lee ,&nbsp;Minjae Kim ,&nbsp;Su J. Kim ,&nbsp;Heonjun Yoon ,&nbsp;Byeng D. Youn","doi":"10.1016/j.ymssp.2025.112742","DOIUrl":"10.1016/j.ymssp.2025.112742","url":null,"abstract":"<div><div>In a multi-sensor system for fault diagnosis, each sensor’s data may contain a different amount of fault-related information, due to different transfer paths of a fault signal to each sensor. Previous research has exploited fault feature analysis to fuse multi-sensor data or simply stacked channels of data for deep learning input, entrusting the information integration task. However, existing methods are hard to identify which sensor’s data dominantly contain fault-related information, since the characteristics of all the sensors’ data gradually get mixed up as the data propagating from input to output converge through the neural network layers. Consequently, the information integration task may not scrutinize the significance of each sensor’s data (<em>i.e.</em>, fault relevance), resulting in information loss. Therefore, this paper proposes a multi-sensor information integration framework that accounts for fault relevance of each sensor for fault diagnosis of rotating machineries. Inspired by the mechanism of a convolutional neural network (CNN), which extracts similar recurrent features in an image by the shared identical convolution kernel (the stationarity) while preserving the spatial information (the locality), a novel kernel manipulation strategy is suggested to generate a multi-sensor feature map. In the proposed method, each sensor’s data share an identical convolution kernel (the stationarity), while the unique characteristic of each sensor’s data is preserved by depth-wise convolution (the locality). Then, a sensor priority evaluation module is designed to weight fault relevance of each sensor, rather than the channel itself. Finally, an averaged attention score is multiplied by the multi-sensor feature map, thereby resulting in a fault-relevance feature map. Two experimental validation results show that the multi-sensor information is appropriately integrated proportional to the amount of the fault-related information in each sensor’s data. It is also confirmed that the proposed method outperforms existing multi-sensor fault diagnosis methods.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112742"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835214","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":"Efficient energy conversion in low-voltage antiphase rotational energy harvesters using a mechanical motion-based rectifier","authors":"Rui Zhong ,&nbsp;Chung Ket Thein ,&nbsp;Dunant Halim ,&nbsp;John Xu","doi":"10.1016/j.ymssp.2025.112755","DOIUrl":"10.1016/j.ymssp.2025.112755","url":null,"abstract":"<div><div>Bridge rectifiers play a critical role in converting AC power into DC power in energy harvesting systems, often using an H-bridge topology with four diodes. However, for low-voltage rotational energy harvesters (REHs), the voltage drop caused by traditional rectifiers critically reduces conversion efficiency. To address this, a novel mechanical motion-based rectifier featuring a dynamic contact holder is proposed for antiphase REHs, eliminating voltage thresholds of 0.2 to 0.7 V and significantly enhancing AC/DC conversion efficiency. This design also reduces the number of diodes required, simplifying the circuit and lowering costs. Bench tests show a 29.9 % increase in load voltage, while application tests demonstrate a 19.43 % increase in effective operating time of an electronic device. The rectifier voltage ratios of 0.517 and 0.996 when replacing high-threshold diode rectifiers, underscoring its superior performance under low coil relative speeds and low load impedance. The innovative rectifier ensures stable and efficient energy transfer, even under fault conditions such as misalignment. This technology has potential applications beyond electromagnetic REHs including piezoelectric and triboelectric energy harvesting systems, offering a promising avenue for advancing energy harvesting efficiency.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112755"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835213","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":"Uncertainty characterization and propagation analysis for pneumatic soft acoustic metamaterial system","authors":"Kun Zhang ,&nbsp;Ning Chen ,&nbsp;Jian Liu ,&nbsp;Michael Beer","doi":"10.1016/j.ymssp.2025.112722","DOIUrl":"10.1016/j.ymssp.2025.112722","url":null,"abstract":"<div><div>Pneumatic soft acoustic metamaterials have gradually attracted attention inspired by pneumatic soft robots. However, current researches ignore the ubiquitous uncertainty factor, which may cause the designed pneumatic soft acoustic metamaterials to fail to achieve the expected performance. In this paper, the influence of uncertainty on pneumatic soft acoustic metamaterial system is investigated. To quantify uncertainties for the system input based on available data, two different uncertainty characterization methods are utilized. By integrating the bootstrap method with kernel density estimation, the input distribution of bounded random model can be determined based on the limited experiment data. For cases with even less experiment data, an unbiased estimation method is introduced to construct interval model. Then, an uncertainty propagation method based on Kriging model and an improved active learning strategy is developed for the pneumatic soft acoustic metamaterial system with bounded hybrid uncertain parameters. Finally, we experimental demonstrated the effectiveness of the uncertainty analysis on the deformation and acoustic property of the pneumatic soft acoustic metamaterial system. The results show the necessity of regarding uncertainties in pneumatic soft acoustic metamaterial system. The study provides a feasible and practical method to model and propagate uncertainty for pneumatic soft acoustic metamaterials systems, which can promote their application in industrial sectors.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":"Article 112722"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829459","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":"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":"A multidirectional broadband pendulum piezoelectric vibration energy harvester utilizing magnetic potential well","authors":"Yiyong Yu,&nbsp;Yazhi Lin,&nbsp;Mingxuan Liu,&nbsp;Jijun Zhou,&nbsp;Jiale Tang,&nbsp;Guojun Li,&nbsp;Zhonghua Zhang,&nbsp;Junwu Kan","doi":"10.1016/j.ymssp.2025.112730","DOIUrl":"10.1016/j.ymssp.2025.112730","url":null,"abstract":"<div><div>Harvesting energy from environmental vibrations using piezoelectric mechanism had attracted much attention for powering wireless sensors over the past decades. To enhance the environmental adaptability of energy harvesters, a multidirectional broadband pendulum piezoelectric vibration energy harvester utilizing magnetic potential well (MPW-PVEH) is proposed in this paper. The MPW-PVEH consists of piezoelectric cantilever beams and a multi-directional pendulum which can oscillate in any direction and pluck the piezoelectric cantilever beam to only yield unidirectional compressive stress. Theoretical analyses and experimental tests were carried out to verify the feasibility of the harvester. The results showed that MPW-PVEH was capable of harvesting horizontal omnidirectional vibration energy between 7 Hz and 10.5 Hz by adjusting the proof mass and torsion radius. Moreover, the operating band of the harvester could be broadened from 4 Hz to 14 Hz by adjusting the magnet distance. The maximum power fluctuation of MPW-PVEH was only 12 % at any horizontal excitation angle, which indicated that MPW-PVEH had good multidirectional consistency. Additionally, the optimum output power of MPW-PVEH could reach 15.6 mW with the load resistance of 90 kΩ at 8 Hz. Furthermore, MPW-PVEH could light 250 LEDs as well as power a temperature sensor and a transmitter module continuously.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"232 ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829619","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}