Mechanical Systems and Signal Processing最新文献

{"title":"Corrigendum to “Modal analysis of non-conservative systems with friction-induced strong nonlinear damping by using response-controlled testing” [Mech. Syst. Signal Process. 221 (2024) 111718]","authors":"Taylan Karaağaçlı, Furkan K. Çelik","doi":"10.1016/j.ymssp.2025.112420","DOIUrl":"10.1016/j.ymssp.2025.112420","url":null,"abstract":"","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112420"},"PeriodicalIF":7.9,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349924","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":"Choosing the right signal processing tools for mechanical systems","authors":"Robert B. Randall ,&nbsp;Jérôme Antoni","doi":"10.1016/j.ymssp.2024.112090","DOIUrl":"10.1016/j.ymssp.2024.112090","url":null,"abstract":"<div><div>Simon Braun was one of the first to recognise the special requirements for processing of signals from mechanical systems, which was why he launched the journal Mechanical Systems and Signal Processing. Mechanical engineers are typically not well trained in signal processing, so signal processing specialists are often recruited from other areas, e.g. electrical engineering, speech processing, acoustics, which often have different requirements. A very important application area of signal processing in mechanical engineering (and mechatronics) is robotics and active control. This requires causal processing in real-time, but that places restrictions on the results, since causal filters have poor characteristics and phase distortion. There are also problems with differentiation, integration, and Hilbert transformation when performed directly in the time domain. Machine Condition Monitoring is another very important area of signal processing for mechanical engineers. This paper shows that causal signal processing is not only not required for Machine Condition Monitoring, even for online monitoring of critical machines, but gives problems and distortions that can be avoided with non-causal signal processing. The paper illustrates the advantages gained using non-causal processing, mostly based on FFT (fast Fourier transform) analysis, for ideal filtration with zero phase shift, as well as error-free differentiation/integration and Hilbert transformation via the frequency domain. However, the circularity of the (non-causal) FFT algorithm gives “wraparound effects”, which must be mitigated. The paper has a short discussion of the situations, apart from active control, where causal processing is of advantage, such as octave-based filtration, and real-time zoom as a precursor to FFT analysis. Finally, the paper discusses the special requirements of machine health indicators obtained by signal processing, because unlike structural health monitoring, they are based more on changes in forcing functions, varying greatly between different machines and components, and not just on dynamic (i.e. modal) properties.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112090"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094446","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":"Advancements and frequency domain error analysis for state-input estimation in virtual points via the augmented Kalman filter","authors":"Julian Staiger ,&nbsp;Rafael S.O. Dias ,&nbsp;Alessandro Zucchini ,&nbsp;Milena Martarelli ,&nbsp;Frank Naets","doi":"10.1016/j.ymssp.2025.112338","DOIUrl":"10.1016/j.ymssp.2025.112338","url":null,"abstract":"<div><div>In this paper, we investigate the usage of the augmented Kalman filter to solve inverse problems by exploiting modal state-space models to characterize forces in a virtual point. Modular engineering approaches require precise interface force characterization, which is challenging to obtain in operation due to the inaccessibility of the interface. However, forces transmitting through connecting components play a crucial role in sound &amp; vibration engineering as well as in lifetime analysis and control. In this work, we compare the estimation of forces transmitting through a rubber mount by using two different strategies that rely on the use of the Virtual Point Transformation (VPT) method. In the first strategy, the forces of interest are determined by applying VPT to transform the estimated set of applied forces. Conversely, in the second methodology, the forces of interest are directly estimated by exploiting input-reduced models defined using the VPT method. This strategy requires the use of an augmented state-space representation of input-reduced models, which is derived in this article. Moreover, a novel approach to determine state-space models reduced into virtual points is proposed, leading to the computation of lower-order models compared to the ones computed with the state-of-the-art technique. The frequency-dependent estimation error of the Augmented Kalman Filter (AKF) scheme, stemming from modelling errors, is derived in a general sense for the cases where displacement and acceleration measurements are exploited. It is shown that the VPT introduces a frequency-dependent error term that biases the estimation obtained with the input-reduced model. This theoretical frequency-dependent error is illustrated in a numerical example, and the results obtained using unreduced and input-reduced models are compared and analysed. Subsequently, an experimental example is analysed, focusing on an industrial rubber bushing. The excitation effects of the forces applied in this component are reconstructed at pre-defined virtual points in both time and frequency domains. Good input estimates are obtained for all kinds of excitation in the time and frequency domains. However, it is found that the results obtained with the input-reduced model are less accurate due to the frequency-dependent error stemming from the VPT, which validates the performed theoretical analysis on the frequency-dependent estimation error of the AKF.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112338"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077500","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":"Periodic sparsity envelope spectrum: An advanced spectral quantity for passive acoustic detection of underwater propeller based on prior information of candidate frequencies","authors":"Weiqi Tong ,&nbsp;Chenheng Lin ,&nbsp;Kelin Wu ,&nbsp;Linlin Cao ,&nbsp;Rui Wu ,&nbsp;Dazhuan Wu","doi":"10.1016/j.ymssp.2025.112369","DOIUrl":"10.1016/j.ymssp.2025.112369","url":null,"abstract":"<div><div>The propeller noise is the primary source of radiated noise from surface ships and submarines. Demodulation techniques such as Detection of Envelope Modulation On Noise (DEMON), narrowband demodulation, and cyclostationary analysis can be used to analyze this noise. However, capturing the characteristic modulation frequencies within the envelope spectrum can be challenging due to far-field effects and complex interference noise. To tackle this challenge, this paper proposes an advanced spectral quantity called the Periodic Sparsity Envelope Spectrum (PSES), which is specifically designed to extract the specific characteristic frequencies of underwater propellers. Firstly, the exact characteristic frequencies are determined using correlated kurtosis with prior knowledge of candidate frequencies. Secondly, a novel adaptive weighting function is proposed based on the periodic sparsity of spectral coherence along the cyclic frequency axis. Moreover, the equal-scale Receiver Operating Characteristic (eROC) indicator is developed to evaluate the demodulation capabilities of different methods and facilitate the automatic detection of the characteristic modulation frequencies of propellers. Ultimately, simulations and experiments on propellers of the water tunnel as well as merchant ships are conducted to verify the effectiveness and superiority of the proposed PSES method.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112369"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077503","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 variational generalized nonlinear mode decomposition for separating crossed chirp modes and dispersive modes of non-stationary signals in mechanical systems","authors":"Hongbing Wang,&nbsp;Shiqian Chen,&nbsp;Wanming Zhai","doi":"10.1016/j.ymssp.2025.112407","DOIUrl":"10.1016/j.ymssp.2025.112407","url":null,"abstract":"<div><div>Recently, an adaptive divide-and-conquer method called variational generalized nonlinear mode decomposition (VGNMD) has been proposed to simultaneously extract chirp modes and dispersive modes from non-stationary signals. However, similar to numerous signal analysis techniques, the VGNMD is unsuitable for analyzing complicated non-stationary signals with crossed modes in mechanical systems because it is based on the assumption that signal modes are strictly separated in the time–frequency (TF) plane. In this paper, an improved VGNMD (I-VGNMD) method is proposed to address this issue. Firstly, considering the advantages of mathematical morphology in image feature extraction, the I-VGNMD introduces a TF-skeleton extraction technique to obtain complete TF skeletons containing crossing features from the TF distribution of the signal. Next, according to the pixel connectivity, a weighted directional skeleton tracking strategy is developed to adaptively select the skeleton tracking path and correctly separate the crossed TF skeletons. Finally, the separated independent skeletons are used as initial instantaneous frequencies or group delays to drive the divide-and-conquer decomposition framework of VGNMD for accurate mode reconstruction. Simulated examples and real-life applications to railway wheel/rail fault diagnosis and rotating target detection are considered to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112407"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077634","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":"Task similarity guided transfer learning for acoustic emission-based rail crack assessment","authors":"Si-Xin Chen ,&nbsp;Lu Zhou ,&nbsp;Yi-Qing Ni ,&nbsp;Jie-Zhong Huang","doi":"10.1016/j.ymssp.2025.112404","DOIUrl":"10.1016/j.ymssp.2025.112404","url":null,"abstract":"<div><div>Transfer learning (TL) has recently experienced significant growth in structural health monitoring (SHM), offering potential solutions to the overfitting problem caused by limited training samples by utilizing knowledge in well-trained models from source to target domain or task. While the discrepancy between source and target domains in TL-aided SHM has been extensively studied, the task similarity (TS) between source and target tasks, a critical aspect of TL within computer science-related disciplines, has not been thoroughly explored. This study introduces a TS-guided TL framework, termed TSTL, to enhance TL performance in SHM applications, specifically focusing on acoustic emission (AE)--based crack assessment. For this target task, we evaluate various candidate source tasks, including sound event identification, semantical “crack” detection, acoustic scene categorization, and image classification. Using representation similarity analysis (RSA) and centered kernel alignment (CKA), we quantify their similarities with the target task. This approach allows for the selection of most suitable model for this assessment task, minimizing subject judgment. Results show that higher TS can improve the F1 score of AE-based crack assessment. In addition, by selecting the labels closest to “crack”, we construct a specific source task with greater TS to the target task which effectively improves TL’s performance. The proposed TSTL approach offers a new perspective for SHM applications in similar contexts.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112404"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077499","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":"Simon Braun Memorial Papers","authors":"","doi":"10.1016/j.ymssp.2024.112104","DOIUrl":"10.1016/j.ymssp.2024.112104","url":null,"abstract":"","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112104"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093046","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":"3D sound radiation reconstruction from camera measurements","authors":"Sofia Baldini ,&nbsp;Gianluca Guernieri ,&nbsp;Domen Gorjup ,&nbsp;Paolo Gardonio ,&nbsp;Janko Slavič ,&nbsp;Roberto Rinaldo","doi":"10.1016/j.ymssp.2025.112400","DOIUrl":"10.1016/j.ymssp.2025.112400","url":null,"abstract":"<div><div>In general, the measurement of the sound radiation field by machinery and partitions requires time-consuming tests, which should be carried out in specially dedicated anechoic/reverberant facilities with calibrated sensors and complex acquisition and post processing equipment. This article introduces a two-step method for the identification from optical measurements of the free-field sound radiation generated by flexural vibrations of closed shells. In the first step, the flexural vibration of the shell is reconstructed with a frequency domain triangulation technique based on short multi-view video acquisitions made with a single high-resolution, high-speed camera. In the second step, the free-field sound radiation is derived from a discretized boundary integral formulation. The study is focused on the identification of the sound radiation from the flexural vibration of a baffled cylinder model structure. The vibration and sound fields reconstructed from the camera measurements are validated against direct measurements taken with a laser scanner vibrometer and a microphone array, respectively. Overall, this research demonstrates that optical methods based on camera measurements can be suitably employed to produce fast and accurate full-field measurements of sound radiation of closed shells (without the need for a dedicated measurement environment, e.g. reverberant, anechoic chambers).</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112400"},"PeriodicalIF":7.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077501","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":"Multi-domain data-driven chatter detection in robotic milling under varied robot poses based on directional attention mechanism","authors":"Xuexin Zhang ,&nbsp;Lianyu Zheng ,&nbsp;Wei Fan ,&nbsp;Lingjun Mao ,&nbsp;Ziyu Li ,&nbsp;Yansheng Cao","doi":"10.1016/j.ymssp.2025.112406","DOIUrl":"10.1016/j.ymssp.2025.112406","url":null,"abstract":"<div><div>Chatter detection is critical during robotic milling process as chatter severely limits its widespread application. However, the physical properties of a robot under different poses vary, resulting in a fluctuating probability of milling chatter, complicating chatter detection. This study proposes a multi-domain data-driven chatter detection method for robotic milling under various robot poses using a directional attention mechanism (DAM). The robotic milling process begins with the collection and analysis of multi-domain data, including time-domain cutting vibration data, frequency-domain passing frequency, and spatial-domain point-cloud data. The milling states were classified as stable, slight chatter, and severe chatter, with the power spectral density (PSD) of the cutting vibration data serving as the primary criterion for determining the milling state. In addition, this study proposes a method for suppressing the effective frequency components of the PSD to improve the chatter frequency discernibility. Second, a DAM is proposed to absorb multi-domain data. Based on this, a multi-scale chatter detection network (DAM-MSCDN) model is proposed. This model uses the multi-scale suppressed PSD as input and incorporates a DAM module to fuse the machined surface’s point cloud information. The designed DAM module effectively improved the model’s robustness and accuracy. Finally, different states of robotic milling in various poses were analyzed experimentally. In addition, the experimental results verified that the DAM-MSCDN model could accurately detect both high-frequency and low-frequency chatter in robotic milling under various robot poses.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112406"},"PeriodicalIF":7.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077504","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":"Bayesian generative kernel Gaussian process regression","authors":"Sin-Chi Kuok ,&nbsp;Shuang-Ao Yao ,&nbsp;Ka-Veng Yuen ,&nbsp;Wang-Ji Yan ,&nbsp;Mark Girolami","doi":"10.1016/j.ymssp.2025.112395","DOIUrl":"10.1016/j.ymssp.2025.112395","url":null,"abstract":"<div><div>The Bayesian generative kernel Gaussian process regression (BGKGPR), a novel progressive probabilistic approach for nonparametric modeling with an optimal generative kernel, is proposed. In Gaussian process (GP) regression, a kernel is assigned to represent the similarity between the input data. Conventional kernels are assigned as the commonly used kernels with all input variables, and a trial-and-error procedure is applied to obtain the finalized kernel. However, an improper choice of the kernel type and/or redundant input variables can significantly degrade the modeling performance. To address this problem, the proposed approach provides a generative kernel augmentation scheme to develop the optimal kernel with the appropriate input variables. The scheme starts with a candidate kernel set. By adopting more features, these candidates evolve as augmented kernels. A Bayesian indicator is formulated to assess the performance of the potential kernels. Hence, the set of kernels that strike the optimal balance between fitting capacity and robustness is chosen for further enhancement. The generation procedure is conducted iteratively until further augmentation ceases to provide considerable improvement in the kernel performance. The proposed approach has three appealing features. First, the optimal kernel with the appropriate input variables can be generated. Second, the resultant kernel can be obtained efficiently in an automatic manner. Third, the uncertainty of all estimates can be quantified. To illustrate the efficacy of the proposed approach, two numerical examples and a case study with three-year continuous monitoring of a 22-story reinforced concrete building are presented.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"227 ","pages":"Article 112395"},"PeriodicalIF":7.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077523","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}