Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112145
Zuoyi Chen, Hong-Zhong Huang, Zhongwei Deng, Jun Wu
Data-driven fault detection (FD) or diagnosis methods are key technologies to ensure safe operation of rotating machinery. These methods rely on a requisite volume of fault data. However, acquiring fault data from rotating machinery is typically problematic and can be entirely unattainable. The critical challenge is to accurately detect and localize the fault states of rotating machinery under the absence of any fault data. Therefore, a newly shrinkage Mamba relation network (SMRN) with out-of-distribution data (OODD) augmentation is proposed for FD and localization in rotating machinery with zero-faulty data. Firstly, the corresponding sensors are arranged for the key detection locations on the rotating machinery. The data generator is referenced to generate OODD for the health data at each detection locations, assisting in mining of intrinsic state information from health data. Then, feature pairs are built in health data and OODD to reveal inter-state attribute relationships. Finally, the location of faults in rotating machinery is determined by evaluating the similarity between feature pairs at each detection location. The SMRN method effectiveness is verified by using self-built propulsion shaft system experiments and rolling bearing cases. The experimental results show the SMRN method can effectively detect and localize fault state of rotating machinery in multiple fault modes, compound fault scenarios, and variable operating conditions.
{"title":"Shrinkage mamba relation network with out-of-distribution data augmentation for rotating machinery fault detection and localization under zero-faulty data","authors":"Zuoyi Chen, Hong-Zhong Huang, Zhongwei Deng, Jun Wu","doi":"10.1016/j.ymssp.2024.112145","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112145","url":null,"abstract":"Data-driven fault detection (FD) or diagnosis methods are key technologies to ensure safe operation of rotating machinery. These methods rely on a requisite volume of fault data. However, acquiring fault data from rotating machinery is typically problematic and can be entirely unattainable. The critical challenge is to accurately detect and localize the fault states of rotating machinery under the absence of any fault data. Therefore, a newly shrinkage Mamba relation network (SMRN) with out-of-distribution data (OODD) augmentation is proposed for FD and localization in rotating machinery with zero-faulty data. Firstly, the corresponding sensors are arranged for the key detection locations on the rotating machinery. The data generator is referenced to generate OODD for the health data at each detection locations, assisting in mining of intrinsic state information from health data. Then, feature pairs are built in health data and OODD to reveal inter-state attribute relationships. Finally, the location of faults in rotating machinery is determined by evaluating the similarity between feature pairs at each detection location. The SMRN method effectiveness is verified by using self-built propulsion shaft system experiments and rolling bearing cases. The experimental results show the SMRN method can effectively detect and localize fault state of rotating machinery in multiple fault modes, compound fault scenarios, and variable operating conditions.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"99 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665633","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112114
Xu Chen, Wen Han, Zhousuo Zhang
Looseness detection of complex multi-bolted flange joints has long been an important problem to be focused on, especially for the scene of unknown multi-bolt loosening at the same time. In this study, a stable, efficient and robust guided wave recognition method for multi-bolt loosening is proposed for the first time by taking long-term monitoring data. This method studies the nonlinear characteristics of transmitted wave energy with bolt preload by formula. Then, a novel probability indicator reflecting the loosening position is proposed and a prior prediction model of bolt loosening degree is established. The prediction model is based on prior data fitting in a small number of working conditions, which has obvious advantages over deep learning. The simulation and experimental results based on flange pipes show that the proposed indicator can effectively determine the loosening positions of multiple bolts, and the prediction model also performs well in degree recognition. The proposed detection method has great potential in real-time monitoring applications by virtue of its high sensitivity to the loosening of multi-bolted joint structures.
{"title":"Loosening state monitoring and identification of multi-bolted flange joints based on nonlinear wave energy transmission","authors":"Xu Chen, Wen Han, Zhousuo Zhang","doi":"10.1016/j.ymssp.2024.112114","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112114","url":null,"abstract":"Looseness detection of complex multi-bolted flange joints has long been an important problem to be focused on, especially for the scene of unknown multi-bolt loosening at the same time. In this study, a stable, efficient and robust guided wave recognition method for multi-bolt loosening is proposed for the first time by taking long-term monitoring data. This method studies the nonlinear characteristics of transmitted wave energy with bolt preload by formula. Then, a novel probability indicator reflecting the loosening position is proposed and a prior prediction model of bolt loosening degree is established. The prediction model is based on prior data fitting in a small number of working conditions, which has obvious advantages over deep learning. The simulation and experimental results based on flange pipes show that the proposed indicator can effectively determine the loosening positions of multiple bolts, and the prediction model also performs well in degree recognition. The proposed detection method has great potential in real-time monitoring applications by virtue of its high sensitivity to the loosening of multi-bolted joint structures.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"13 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665634","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112138
Saisai Chen, Tong Zhou, Wei Fan, Yuyong Xiong
Blade is the core working components of an aero-engine, and its blade tip clearance (BTC) exerts a direct influence on the efficiency and safety of the engine. Aiming to extract the BTC region from the echo signal, a novel microwave-based dynamic measurement system through nonlinear I/Q imbalance correction is proposed. Firstly, to achieve accurately correction of the sensor under non-linear amplitude attenuation, a new correction method is proposed to map the amplitude decay attenuation into the imbalance parameters distribution. Secondly, to effectively localize the extraction BTC region, the Amplitude-phase Half Wave Extraction (APH) is proposed, which utilizes squared I/Q amplitude information to determine the BTC position and extract clearance information. Finally, in comparison to existing methods, the proposed algorithm exhibits excellent performance in signal correction under non-linear amplitude attenuation and achieves high-accurate BTC extraction. Experimental studies show a mean absolute error below 2 μm and a repeatability mean error of 0.154 μm for a BTC variation of 0.17 mm.
{"title":"A novel microwave-based dynamic measurement method for blade tip clearance through nonlinear I/Q imbalance correction","authors":"Saisai Chen, Tong Zhou, Wei Fan, Yuyong Xiong","doi":"10.1016/j.ymssp.2024.112138","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112138","url":null,"abstract":"Blade is the core working components of an aero-engine, and its blade tip clearance (BTC) exerts a direct influence on the efficiency and safety of the engine. Aiming to extract the BTC region from the echo signal, a novel microwave-based dynamic measurement system through nonlinear I/Q imbalance correction is proposed. Firstly, to achieve accurately correction of the sensor under non-linear amplitude attenuation, a new correction method is proposed to map the amplitude decay attenuation into the imbalance parameters distribution. Secondly, to effectively localize the extraction BTC region, the Amplitude-phase Half Wave Extraction (APH) is proposed, which utilizes squared I/Q amplitude information to determine the BTC position and extract clearance information. Finally, in comparison to existing methods, the proposed algorithm exhibits excellent performance in signal correction under non-linear amplitude attenuation and achieves high-accurate BTC extraction. Experimental studies show a mean absolute error below 2 μm and a repeatability mean error of 0.154 μm for a BTC variation of 0.17 mm.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665632","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112116
Wenjie Bao, Songyong Liu, Zhen Liu, Fucai Li
Time-frequency (TF) rearrangement methods represented by synchrosqueezing transform (SST) and synchroextracting transform (SET) have recently been considered efficient tools for obtaining time-varying features of nonstationary signals. However, so far improving concentration and accuracy is still an open problem, especially for the signal with strongly time-varying instantaneous frequency (IF), due to the fact that they cannot achieve an accurate and generalized IF estimation. In order to address this problem, we introduce a new TF analysis method termed as generalized synchroextracting transform (GSET) by constructing a general signal model. Our first contribution in this study is proposing a new computational framework to derive the generalized explicit formula of Nth-order IF estimation, which can realize the programming of any order IF. By extracting the energy of the TF representation (TFR) on the estimated IF, a more concentrated and accurate TFR can be obtained. Our second contribution is giving a more accurate signal reconstruction method of the TFR from a new perspective. It solves the problem that the reconstruction method of the synchroextracting transform cannot be extended to the Nth-order. Numerical analysis of multicomponent simulated signal demonstrates that the GSET can effectively improve the TF readability of strongly time-varying signal and accurately reconstruct the signal from the TFR. Moreover, experiment and application results verify that the proposed method can be used for fault diagnosis of rotating machinery.
以同步萃取变换（SST）和同步提取变换（SET）为代表的时频（TF）重排方法最近被认为是获取非稳态信号时变特征的有效工具。然而，到目前为止，提高集中度和准确度仍是一个未决问题，尤其是对于具有强烈时变瞬时频率（IF）的信号，因为它们无法实现准确和通用的 IF 估计。为了解决这个问题，我们通过构建一个通用信号模型，引入了一种新的 TF 分析方法，称为广义同步提取变换（GSET）。我们在这项研究中的第一个贡献是提出了一个新的计算框架，以推导出 N 阶中频估计的广义显式公式，它可以实现任意阶中频的编程。通过提取估计中频上的 TF 表示（TFR）能量，可以获得更集中、更精确的 TFR。我们的第二个贡献是从新的角度给出了一种更精确的 TFR 信号重构方法。它解决了同步提取变换的重构方法无法扩展到 N 阶的问题。对多分量模拟信号的数值分析表明，GSET 能有效提高强时变信号的 TF 可读性，并能从 TFR 准确地重建信号。此外，实验和应用结果验证了所提出的方法可用于旋转机械的故障诊断。
{"title":"Generalized synchroextracting transform: Algorithm and applications","authors":"Wenjie Bao, Songyong Liu, Zhen Liu, Fucai Li","doi":"10.1016/j.ymssp.2024.112116","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112116","url":null,"abstract":"Time-frequency (TF) rearrangement methods represented by synchrosqueezing transform (SST) and synchroextracting transform (SET) have recently been considered efficient tools for obtaining time-varying features of nonstationary signals. However, so far improving concentration and accuracy is still an open problem, especially for the signal with strongly time-varying instantaneous frequency (IF), due to the fact that they cannot achieve an accurate and generalized IF estimation. In order to address this problem, we introduce a new TF analysis method termed as generalized synchroextracting transform (GSET) by constructing a general signal model. Our first contribution in this study is proposing a new computational framework to derive the generalized explicit formula of <mml:math altimg=\"si10.svg\"><mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">th</mml:mi></mml:mrow></mml:msup></mml:math>-order IF estimation, which can realize the programming of any order IF. By extracting the energy of the TF representation (TFR) on the estimated IF, a more concentrated and accurate TFR can be obtained. Our second contribution is giving a more accurate signal reconstruction method of the TFR from a new perspective. It solves the problem that the reconstruction method of the synchroextracting transform cannot be extended to the <mml:math altimg=\"si10.svg\"><mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">th</mml:mi></mml:mrow></mml:msup></mml:math>-order. Numerical analysis of multicomponent simulated signal demonstrates that the GSET can effectively improve the TF readability of strongly time-varying signal and accurately reconstruct the signal from the TFR. Moreover, experiment and application results verify that the proposed method can be used for fault diagnosis of rotating machinery.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"43 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665642","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112131
Chi Zhang, Ziyue Lu, Xingtian Li, Yifeng Zhang, Xiaoyu Guo
Displacement plays a pivotal role in bridge assessment, but accurate displacement monitoring remains a challenging task. Unmanned Aerial Vehicles (UAVs) provide a cost-effective, time-efficient, and high maneuverability alternative to infrastructure monitoring, as they overcome the spatial limitations of the fixed camera and acquire the high-resolution image sequence. However, the measurement accuracy is often affected by the movement of the UAV. To address these constraints, this study proposed a computer vision-based nontarget displacement measurement method and a two-stage UAV movement correction method using fixed point and variational mode decomposition (VMD). Initially, the adaptive fusion of deep features and shallow features can efficiently encode the informative representation of the natural texture on the structural surface. Subsequently, the movement of the UAV is eliminated by stationary fixed points (Step Ⅰ) and VMD techniques (Step Ⅱ). Finally, the performance of the proposed methodology is verified with the field tests on a concrete wall and an arch bridge. Through mode decomposition and reconstruction, the measurement accuracy is greatly improved compared to the correction method only using fixed points, which proves the reliability and effectiveness of the proposed non-target displacement measurement method.
{"title":"A two-stage correction method for UAV movement-induced errors in non-target computer vision-based displacement measurement","authors":"Chi Zhang, Ziyue Lu, Xingtian Li, Yifeng Zhang, Xiaoyu Guo","doi":"10.1016/j.ymssp.2024.112131","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112131","url":null,"abstract":"Displacement plays a pivotal role in bridge assessment, but accurate displacement monitoring remains a challenging task. Unmanned Aerial Vehicles (UAVs) provide a cost-effective, time-efficient, and high maneuverability alternative to infrastructure monitoring, as they overcome the spatial limitations of the fixed camera and acquire the high-resolution image sequence. However, the measurement accuracy is often affected by the movement of the UAV. To address these constraints, this study proposed a computer vision-based nontarget displacement measurement method and a two-stage UAV movement correction method using fixed point and variational mode decomposition (VMD). Initially, the adaptive fusion of deep features and shallow features can efficiently encode the informative representation of the natural texture on the structural surface. Subsequently, the movement of the UAV is eliminated by stationary fixed points (Step Ⅰ) and VMD techniques (Step Ⅱ). Finally, the performance of the proposed methodology is verified with the field tests on a concrete wall and an arch bridge. Through mode decomposition and reconstruction, the measurement accuracy is greatly improved compared to the correction method only using fixed points, which proves the reliability and effectiveness of the proposed non-target displacement measurement method.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"22 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665629","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112122
Collin Treacy, Dalton Stein, David Chelidze
Hazardous failures of engineering structures can be prevented by implementing passive vibration absorbers. Although tuned mass dampers (TMD) are used most frequently in practice, nonlinear energy sinks (NES) offer a broader frequency performance due to their targeted energy transfer (TET) mechanisms. However, this behavior occurs only in a limited amplitude range. In this work, the vibration suppression of novel monostable and bistable magnetic rotary nonlinear energy sinks (MRNESs) are studied numerically and experimentally over a range of excitation magnitudes for impulse and harmonic excitation. The MRNESs are tuned to achieve hybrid TMD and NES-like behavior. The medley of in-well, cross-well, and rotational TET mechanisms responsible for their performance are related to their underlying Hamiltonian systems and lower boundaries of chaos. For impulse excitation, rotational, subharmonic, and nonlinear beat responses lead to efficient energy dissipation. For harmonic excitation, the MRNEs’ frequency responses can resemble a TMDs’ or exhibit chaotic-like cross-well and rotational strongly modulated responses. Consequently, MRNESs can overcome the shortcomings of many NESs, which are inefficient at low excitation magnitudes, while also outperforming linear TMDs when the systems’ parameters are detuned at large excitation magnitudes, for both impulse and harmonic excitation. The MRNESs’ TET mechanisms and efficient performance over a broad range of excitation magnitudes were validated experimentally for both types of excitation. The MRNES may be more viable for practical use than other hybrid NESs since it is compact, highly customizable, and does not rely on impacts or complicated spring arrangements for its non-linearity.
采用被动减震器可以防止工程结构发生危险故障。虽然调谐质量阻尼器（TMD）在实践中使用最为频繁，但非线性能量吸收器（NES）因其定向能量转移（TET）机制而具有更宽的频率性能。然而，这种行为只出现在有限的振幅范围内。在这项工作中，我们通过数值和实验研究了新型单稳态和双稳态磁旋转非线性能量汇（MRNES）在脉冲和谐波激励的激励幅度范围内的振动抑制能力。对 MRNES 进行了调整，以实现类似于 TMD 和 NES 的混合行为。造成其性能的井内、跨井和旋转 TET 机制与它们的底层哈密顿系统和混沌下边界有关。对于脉冲激励，旋转、次谐和非线性节拍响应可实现高效的能量耗散。对于谐波激励，MRNEs 的频率响应可能类似于 TMDs 的频率响应，或表现出类似于混沌的交叉井和旋转强调制响应。因此，MRNES 可以克服许多 NES 在低激励幅值时效率低下的缺点，同时在脉冲和谐波激励下，当系统参数在大激励幅值时失谐时，MRNES 的性能也优于线性 TMD。实验验证了 MRNES 的 TET 机制以及在两种激励下在宽激励幅度范围内的高效性能。由于 MRNES 结构紧凑、高度可定制，而且其非线性不依赖于冲击或复杂的弹簧布置，因此在实际应用中可能比其他混合 NES 更为可行。
{"title":"Multifaceted vibration absorption of a rotating magnetic nonlinear energy sink","authors":"Collin Treacy, Dalton Stein, David Chelidze","doi":"10.1016/j.ymssp.2024.112122","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112122","url":null,"abstract":"Hazardous failures of engineering structures can be prevented by implementing passive vibration absorbers. Although tuned mass dampers (TMD) are used most frequently in practice, nonlinear energy sinks (NES) offer a broader frequency performance due to their targeted energy transfer (TET) mechanisms. However, this behavior occurs only in a limited amplitude range. In this work, the vibration suppression of novel monostable and bistable magnetic rotary nonlinear energy sinks (MRNESs) are studied numerically and experimentally over a range of excitation magnitudes for impulse and harmonic excitation. The MRNESs are tuned to achieve hybrid TMD and NES-like behavior. The medley of in-well, cross-well, and rotational TET mechanisms responsible for their performance are related to their underlying Hamiltonian systems and lower boundaries of chaos. For impulse excitation, rotational, subharmonic, and nonlinear beat responses lead to efficient energy dissipation. For harmonic excitation, the MRNEs’ frequency responses can resemble a TMDs’ or exhibit chaotic-like cross-well and rotational strongly modulated responses. Consequently, MRNESs can overcome the shortcomings of many NESs, which are inefficient at low excitation magnitudes, while also outperforming linear TMDs when the systems’ parameters are detuned at large excitation magnitudes, for both impulse and harmonic excitation. The MRNESs’ TET mechanisms and efficient performance over a broad range of excitation magnitudes were validated experimentally for both types of excitation. The MRNES may be more viable for practical use than other hybrid NESs since it is compact, highly customizable, and does not rely on impacts or complicated spring arrangements for its non-linearity.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"106 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665631","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}
Pub Date : 2024-11-17DOI: 10.1016/j.ymssp.2024.112084
Nathan Dwek, Vasileios Dimopoulos, Dennis Janssens, Matteo Kirchner, Elke Deckers, Frank Naets
This article proposes a practical and effective damage identification approach for plate-like structures. This approach measures the back scattering caused by damage, and decomposes it into individual contributions from each defect, using the responses of the healthy structure as a dictionary. A data-driven model is used, which circumvents the challenge of numerically simulating the effect of damage, yet does not require training data from known-damaged structures. The decomposition itself is performed using sparsity-promoting optimization, which reduces the number of required measurements and streamlines the inspection procedure. A novel frequency-coupled method is proposed to obtain the desired spatial sparsity of the estimated damage, which results in improved accuracy compared to the previously proposed frequency-decoupled method. Damage identification is demonstrated on a 600mm×600mm composite plate, using a single accelerometer and 7 impact hammer hits. The performance is evaluated on 6 damage scenarios, for 7 accelerometer positions, and for SNRs ranging from 30 to 0dB. Detection and localization are shown to be excellent up to 5 defects and down to 15dB SNR, and to remain robust and predictable outside of that range. These results are compared to reference methods and a significant improvement is observed.
{"title":"Damage identification in plate-like structures using frequency-coupled [formula omitted]-based sparse estimation","authors":"Nathan Dwek, Vasileios Dimopoulos, Dennis Janssens, Matteo Kirchner, Elke Deckers, Frank Naets","doi":"10.1016/j.ymssp.2024.112084","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112084","url":null,"abstract":"This article proposes a practical and effective damage identification approach for plate-like structures. This approach measures the back scattering caused by damage, and decomposes it into individual contributions from each defect, using the responses of the healthy structure as a dictionary. A data-driven model is used, which circumvents the challenge of numerically simulating the effect of damage, yet does not require training data from known-damaged structures. The decomposition itself is performed using sparsity-promoting optimization, which reduces the number of required measurements and streamlines the inspection procedure. A novel frequency-coupled method is proposed to obtain the desired spatial sparsity of the estimated damage, which results in improved accuracy compared to the previously proposed frequency-decoupled method. Damage identification is demonstrated on a <mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mrow><mml:mtext>600</mml:mtext><mml:mspace width=\"0.16667em\"></mml:mspace><mml:mtext>mm</mml:mtext><mml:mo linebreak=\"goodbreak\" linebreakstyle=\"after\">×</mml:mo><mml:mtext>600</mml:mtext><mml:mspace width=\"0.16667em\"></mml:mspace><mml:mtext>mm</mml:mtext></mml:mrow></mml:math> composite plate, using a single accelerometer and 7 impact hammer hits. The performance is evaluated on 6 damage scenarios, for 7 accelerometer positions, and for SNRs ranging from 30 to 0<ce:hsp sp=\"0.16667\"></ce:hsp>dB. Detection and localization are shown to be excellent up to 5 defects and down to 15<ce:hsp sp=\"0.16667\"></ce:hsp>dB SNR, and to remain robust and predictable outside of that range. These results are compared to reference methods and a significant improvement is observed.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"249 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665637","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}
Due to extensive detection range and high sensitivity to defects, ultrasonic Lamb waves are extensively studied in the fields of Nondestructive Testing and Structural Health Monitoring. In scenarios where the material parameters or geometric parameters of the waveguide are unknown, the dispersion relation of the guided wave cannot be calculated by the forward model. Consequently, it becomes imperative to extract wave propagation characteristics of Lamb wave from the acquired Lamb wave data. This paper presents a multitask complex hierarchical sparse Bayesian learning (MuCHSBL) method which is aimed at enhancing the efficacy of the dispersion relation solution by considering the continuity of the recovered dispersion curve in the frequency-wavenumber domain. Furthermore, the posterior distributions quantified by MuCHSBL are employed to optimize the placement of measurement points. Numerical and experimental studies are conducted to verify the effectiveness of the proposed method. Comparison analysis with the conventional approach demonstrates the significant enhancement in accuracy of recovering dispersion curves by the proposed method.
{"title":"Outlier-resistant guided wave dispersion curve recovery and measurement placement optimization base on multitask complex hierarchical sparse Bayesian learning","authors":"Shicheng Xue, Wensong Zhou, Yong Huang, Lam Heung Fai, Hui Li","doi":"10.1016/j.ymssp.2024.112137","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112137","url":null,"abstract":"Due to extensive detection range and high sensitivity to defects, ultrasonic Lamb waves are extensively studied in the fields of Nondestructive Testing and Structural Health Monitoring. In scenarios where the material parameters or geometric parameters of the waveguide are unknown, the dispersion relation of the guided wave cannot be calculated by the forward model. Consequently, it becomes imperative to extract wave propagation characteristics of Lamb wave from the acquired Lamb wave data. This paper presents a multitask complex hierarchical sparse Bayesian learning (MuCHSBL) method which is aimed at enhancing the efficacy of the dispersion relation solution by considering the continuity of the recovered dispersion curve in the frequency-wavenumber domain. Furthermore, the posterior distributions quantified by MuCHSBL are employed to optimize the placement of measurement points. Numerical and experimental studies are conducted to verify the effectiveness of the proposed method. Comparison analysis with the conventional approach demonstrates the significant enhancement in accuracy of recovering dispersion curves by the proposed method.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"128 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665630","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}
Pub Date : 2024-11-14DOI: 10.1016/j.ymssp.2024.112133
Ke Yan, Shuaijun Ma, Bin Fang, Fei Chen, Jun Hong, Pan Zhang
Cylindrical roller bearings are inevitably impacted by external moments or mounting error, which leads to uneven load distribution on the rollers and triggers deformation. Current methods are insufficient to simulate the deformation while ensuring solution accuracy. To address this, a new slicing approach is innovatively proposed in the paper, where springs are added between the neighboring slices to simulate the elastic deformation of the roller under external loads. Compared with the existing methods, the method presented in this paper has the best agreement with the finite element model. On this basis, a dynamic model for cylindrical roller bearing with roller deformation is further developed and verified experimentally. Finally, the sliding behavior inside the bearing under three typical conditions is investigated. A rich spectrum of frequencies emerges in the bearing contact load between the roller and raceway because of the roller deformation. These are all integer multiples of the roller passage frequency. An interesting phenomenon is observed that the sliding velocity is strongly influenced by the orbital speed of the roller compared to its rotational speed. The modeling and analysis in this paper provide new directions to the future work.
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Pub Date : 2024-11-14DOI: 10.1016/j.ymssp.2024.112135
Taoning Zhu, Yu Ren, Huailong Shi, Yunguang Ye, Piji Feng, Zhenhua Su, Chunxing Yao, Guangtong Ma
As urbanization progresses, metropolitan transit vehicles are encountering a growing frequency of curved pathways, which presents challenges pertaining to both the safety of the vehicles and the comfort of the passengers. There is no doubt that reliable acquisition of wheel-rail force is critical, since it has great significance for the safety and stability of vehicle operation. However, conventional wheel-rail force measurement methods are costly and difficult to use on in-service vehicles. A data-driven approach to inverting the wheel-rail force will overcome the above problems. In this work, a transfer learning-based residual long short-term memory neural network with temporal pattern attention mechanism (TPA-ResLSTM) is proposed to realize real-time monitoring of wheel-rail force, even in scenarios where the dataset is deficient in sufficient features. Initially, a learnable wheel-rail force inversion neural network model is developed based on the physical relationship that exists between the wheel-rail force and acceleration. Subsequently, a dynamic model for a B-type metro vehicle is utilized to simulate various scenarios, serving as a virtual source to provide data for the neural network. Afterward, the performance of the model is synthetically validated by the ablation study and field experimental data. Finally, the deep learning model is further improved by the transfer learning network, whose performance is comprehensively evaluated using limited data. The results show that the inversion model still has remarkable accuracy, in which the coefficient of determination is more than 0.9, under the case of limited training data. The proposed methodology diminishes the data requirements for the network while facilitating real-time monitoring and feedback regarding wheel-rail forces, thereby enhancing the realism of operational safety assessments for trains.
随着城市化进程的推进，城市轨道交通车辆越来越频繁地行驶在弯曲的道路上，这给车辆的安全性和乘客的舒适性都带来了挑战。毫无疑问，可靠地获取轮轨力至关重要，因为它对车辆运行的安全性和稳定性具有重大意义。然而，传统的轮轨力测量方法成本高昂，且难以在在用车辆上使用。数据驱动的轮轨力反演方法将克服上述问题。本研究提出了一种基于迁移学习的残差长短期记忆神经网络与时态模式注意机制（TPA-ResLSTM），即使在数据集缺乏足够特征的情况下，也能实现轮轨力的实时监测。首先，基于轮轨力和加速度之间存在的物理关系，建立了一个可学习的轮轨力反转神经网络模型。随后，利用 B 型地铁车辆的动态模型模拟各种场景，作为虚拟源为神经网络提供数据。之后，通过烧蚀研究和现场实验数据对模型的性能进行了综合验证。最后，通过迁移学习网络进一步改进了深度学习模型，并利用有限的数据对其性能进行了综合评估。结果表明，在训练数据有限的情况下，反演模型仍然具有显著的准确性，其中决定系数大于 0.9。所提出的方法既降低了对网络数据的要求，又便于对轮轨力进行实时监测和反馈，从而提高了列车运行安全评估的真实性。
{"title":"Wheel-rail force inversion via transfer learning-based residual LSTM neural network with temporal pattern attention mechanism","authors":"Taoning Zhu, Yu Ren, Huailong Shi, Yunguang Ye, Piji Feng, Zhenhua Su, Chunxing Yao, Guangtong Ma","doi":"10.1016/j.ymssp.2024.112135","DOIUrl":"https://doi.org/10.1016/j.ymssp.2024.112135","url":null,"abstract":"As urbanization progresses, metropolitan transit vehicles are encountering a growing frequency of curved pathways, which presents challenges pertaining to both the safety of the vehicles and the comfort of the passengers. There is no doubt that reliable acquisition of wheel-rail force is critical, since it has great significance for the safety and stability of vehicle operation. However, conventional wheel-rail force measurement methods are costly and difficult to use on in-service vehicles. A data-driven approach to inverting the wheel-rail force will overcome the above problems. In this work, a transfer learning-based residual long short-term memory neural network with temporal pattern attention mechanism (TPA-ResLSTM) is proposed to realize real-time monitoring of wheel-rail force, even in scenarios where the dataset is deficient in sufficient features. Initially, a learnable wheel-rail force inversion neural network model is developed based on the physical relationship that exists between the wheel-rail force and acceleration. Subsequently, a dynamic model for a B-type metro vehicle is utilized to simulate various scenarios, serving as a virtual source to provide data for the neural network. Afterward, the performance of the model is synthetically validated by the ablation study and field experimental data. Finally, the deep learning model is further improved by the transfer learning network, whose performance is comprehensively evaluated using limited data. The results show that the inversion model still has remarkable accuracy, in which the coefficient of determination is more than 0.9, under the case of limited training data. The proposed methodology diminishes the data requirements for the network while facilitating real-time monitoring and feedback regarding wheel-rail forces, thereby enhancing the realism of operational safety assessments for trains.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"50 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665635","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}
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