Pub Date : 2025-02-17DOI: 10.1016/j.ymssp.2025.112475
Jianing Liu , Hongrui Cao , Jaspreet Singh Dhupia , Madhurjya Dev Choudhury , Yang Fu , Siwen Chen , Jinhui Li , Bin Yv
Cross-machine fault detection is crucial due to the challenges of data labeling. While domain adaptation methods facilitate diagnosis across rotating machines, they often require data sharing, which is impractical due to privacy concerns and large data transmission. Although domain generalization and source-free unsupervised domain adaptation (SFUDA) methods address privacy issues, most fail to consider dynamic distribution shifts within and between domains, limiting their effectiveness. To overcome this challenge, an adaptive SFUDA method named AI3M is proposed. The AI3M pre-trains a source model with intra- and inter-domain information maximization loss to reduce distribution shifts within and between domains, and then adapts the model with a target-guided adaptation strategy to minimize the dynamic gap between different machines. Experiments on datasets from 11 wind turbines across 8 wind farms show that the proposed method outperforms state-of-the-art DG and SFUDA approaches, achieving superior cross-machine fault detection performance.
{"title":"An adaptive source-free unsupervised domain adaptation method for mechanical fault detection","authors":"Jianing Liu , Hongrui Cao , Jaspreet Singh Dhupia , Madhurjya Dev Choudhury , Yang Fu , Siwen Chen , Jinhui Li , Bin Yv","doi":"10.1016/j.ymssp.2025.112475","DOIUrl":"10.1016/j.ymssp.2025.112475","url":null,"abstract":"<div><div>Cross-machine fault detection is crucial due to the challenges of data labeling. While domain adaptation methods facilitate diagnosis across rotating machines, they often require data sharing, which is impractical due to privacy concerns and large data transmission. Although domain generalization and source-free unsupervised domain adaptation (SFUDA) methods address privacy issues, most fail to consider dynamic distribution shifts within and between domains, limiting their effectiveness. To overcome this challenge, an adaptive SFUDA method named AI3M is proposed. The AI3M pre-trains a source model with intra- and inter-domain information maximization loss to reduce distribution shifts within and between domains, and then adapts the model with a target-guided adaptation strategy to minimize the dynamic gap between different machines. Experiments on datasets from 11 wind turbines across 8 wind farms show that the proposed method outperforms state-of-the-art DG and SFUDA approaches, achieving superior cross-machine fault detection performance.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112475"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430144","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 : 2025-02-17DOI: 10.1016/j.ymssp.2025.112445
S. Karthikeyani, S. Sasipriya, M. Ramkumar
Cardiac arrhythmias (CA) are critical health conditions. In such a case, highly accurate detection leads to better management and, therefore, better treatment. Here, this paper presents a novel high-performance detection framework for cardiac arrhythmias based on advanced signal processing algorithms with deep learning on a Field Programmable Gate Array (FPGA) towards achieving real-time performances and even higher accuracy. ECG signals are initially analyzed by using compressive sensing theory to obtain sparsity, and from that, the adaptive compressive sensing framework is created. This compressive sensing framework adapts the sensing matrix step by step through compression via the Hybrid Reptile Search Algorithm integrated with the Garra Rufa Algorithm (Hyb-RSA-GRA). The adapted sensing matrix renders signal reconstruction efficient through Bayesian Regularization-Backpropagation Neural Network (BRBNN). The new arrhythmia detection framework employs the possibility of higher-order spectral distribution (HoSD) in extracting finer patterns from ECGs that describe arrhythmia. The task of classification uses a pre-trained Graph Convolutional Neural Network (GCNN) acting as a Deep Inference Engine on the FPGA to support real-time, robust identification of the type of arrhythmias such as N (normal beat), S (supraventricular ectopic beat), V (ventricular ectopic beat), F (fusion beat), and U (unidentified beat). The proposed FPGA implementation reveals better performance with high accuracy, sensitivity, specificity, precision, recall, and F1-score with optimized power dissipation, resource utilization, and delay metrics. Furthermore, the compressive sensing framework guarantees low MSE, reduced RMSE, high SNR, and an improved reconstruction probability. All the above results demonstrate the capability of the framework in accurate prediction and hardware efficiency, hence making it a robust solution for cardiac arrhythmia detection.
{"title":"A framework for detecting high-performance cardiac arrhythmias using deep inference engine on FPGA and higher-order spectral distribution","authors":"S. Karthikeyani, S. Sasipriya, M. Ramkumar","doi":"10.1016/j.ymssp.2025.112445","DOIUrl":"10.1016/j.ymssp.2025.112445","url":null,"abstract":"<div><div>Cardiac arrhythmias (CA) are critical health conditions. In such a case, highly accurate detection leads to better management and, therefore, better treatment. Here, this paper presents a novel high-performance detection framework for cardiac arrhythmias based on advanced signal processing algorithms with deep learning on a Field Programmable Gate Array (FPGA) towards achieving real-time performances and even higher accuracy. ECG signals are initially analyzed by using compressive sensing theory to obtain sparsity, and from that, the adaptive compressive sensing framework is created. This compressive sensing framework adapts the sensing matrix step by step through compression via the Hybrid Reptile Search Algorithm integrated with the Garra Rufa Algorithm (Hyb-RSA-GRA). The adapted sensing matrix renders signal reconstruction efficient through Bayesian Regularization-Backpropagation Neural Network (BRBNN). The new arrhythmia detection framework employs the possibility of higher-order spectral distribution (HoSD) in extracting finer patterns from ECGs that describe arrhythmia. The task of classification uses a pre-trained Graph Convolutional Neural Network (GCNN) acting as a Deep Inference Engine on the FPGA to support real-time, robust identification of the type of arrhythmias such as N (normal beat), S (supraventricular ectopic beat), V (ventricular ectopic beat), F (fusion beat), and U (unidentified beat). The proposed FPGA implementation reveals better performance with high accuracy, sensitivity, specificity, precision, recall, and F1-score with optimized power dissipation, resource utilization, and delay metrics. Furthermore, the compressive sensing framework guarantees low MSE, reduced RMSE, high SNR, and an improved reconstruction probability. All the above results demonstrate the capability of the framework in accurate prediction and hardware efficiency, hence making it a robust solution for cardiac arrhythmia detection.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112445"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422023","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 : 2025-02-17DOI: 10.1016/j.ymssp.2025.112458
Cuiying Lin , Yun Kong , Qinkai Han , Xiantao Zhang , Junyu Qi , Meng Rao , Mingming Dong , Hui Liu , Ming J. Zuo , Fulei Chu
Unsupervised multi-source domain adaptation can overcome the limitations associated with insufficient information diversity in single-source domain adaptation for intelligent transfer diagnosis. However, the challenges of time-varying working conditions in practical industrial applications, limitation in single-level information fusion along with lack of multi-level information fusion restrict effective applications of unsupervised multi-source domain adaptation in transfer diagnosis. To address these challenges, this research presents a novel unsupervised multi-level fusion domain adaptation methodology for transfer diagnostics under time-varying working conditions, which employs a multi-level fusion domain adaptation network (MLFDAN). Firstly, a multi-sensor data enhancement and fusion module is proposed by combining continuous wavelet transform with an RGB information fusion, which integrates time–frequency and spatial information from multi-sensors. Then, a squeeze and excitation feature fusion module is designed for feature fusion across both time–frequency and spatial domains, which effectively emphasizes domain-invariant features while suppressing less relevant ones. Subsequently, an adaptive collaborative decision module is developed, which employs a weighted fusion strategy to address strong conflicts among multi-subnet predictions and utilizes consensus-based fusion strategy when multi-subnet predictions align, thus ensuring reliable and robust diagnostics decisions. Finally, a promising MLFDAN framework for transfer diagnosis is proposed by incorporating a dual-component domain adaptation approach that integrates a domain discriminator and multi-kernel maximum mean discrepancy. Numerous experiment results show that the presented MLFDAN methodology effectively adapts to transfer diagnosis scenarios from steady to time-varying working conditions, achieving impressive performances and outperforming several prominent unsupervised transfer diagnosis methodologies.
{"title":"An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions","authors":"Cuiying Lin , Yun Kong , Qinkai Han , Xiantao Zhang , Junyu Qi , Meng Rao , Mingming Dong , Hui Liu , Ming J. Zuo , Fulei Chu","doi":"10.1016/j.ymssp.2025.112458","DOIUrl":"10.1016/j.ymssp.2025.112458","url":null,"abstract":"<div><div>Unsupervised multi-source domain adaptation can overcome the limitations associated with insufficient information diversity in single-source domain adaptation for intelligent transfer diagnosis. However, the challenges of time-varying working conditions in practical industrial applications, limitation in single-level information fusion along with lack of multi-level information fusion restrict effective applications of unsupervised multi-source domain adaptation in transfer diagnosis. To address these challenges, this research presents a novel unsupervised multi-level fusion domain adaptation methodology for transfer diagnostics under time-varying working conditions, which employs a multi-level fusion domain adaptation network (MLFDAN). Firstly, a multi-sensor data enhancement and fusion module is proposed by combining continuous wavelet transform with an RGB information fusion, which integrates time–frequency and spatial information from multi-sensors. Then, a squeeze and excitation feature fusion module is designed for feature fusion across both time–frequency and spatial domains, which effectively emphasizes domain-invariant features while suppressing less relevant ones. Subsequently, an adaptive collaborative decision module is developed, which employs a weighted fusion strategy to address strong conflicts among multi-subnet predictions and utilizes consensus-based fusion strategy when multi-subnet predictions align, thus ensuring reliable and robust diagnostics decisions. Finally, a promising MLFDAN framework for transfer diagnosis is proposed by incorporating a dual-component domain adaptation approach that integrates a domain discriminator and multi-kernel maximum mean discrepancy. Numerous experiment results show that the presented MLFDAN methodology effectively adapts to transfer diagnosis scenarios from steady to time-varying working conditions, achieving impressive performances and outperforming several prominent unsupervised transfer diagnosis methodologies.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112458"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430145","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 : 2025-02-16DOI: 10.1016/j.ymssp.2025.112441
Xinyang Ma, Jinkun Liu
This article proposes an adaptive control scheme for multi-flexible manipulators to address the challenges of unknown control directions and time-varying actuator faults, then realize contact force control and vibration suppression. Unlike existing methods that require identical control directions, the proposed approach expands application scope to multi-agent systems with nonidentical control directions. By introducing extended Nussbaum functions with saturated amplitudes, the scheme prevents oscillations caused by the unbounded growth of traditional Nussbaum functions. The interdependent design of multiple extended Nussbaum functions enhances the robustness against actuator faults and unknown control dynamics. The controllers achieve consensus in contact force control while suppressing vibrations, ensuring global stability and asymptotic convergence without relying on force sensors, thus avoiding sensor noise. Simulation results demonstrate the effectiveness of the proposed approach in dealing with the unknown control dynamics and realizing control objectives. The theoretical analysis and simulation results demonstrate that the proposed approach is a promising solution for practical multi-agent applications including force control of multi-flexible manipulators.
{"title":"Adaptive contact-force control and vibration suppression for multi flexible manipulators with unknown control directions and time-varying actuator faults","authors":"Xinyang Ma, Jinkun Liu","doi":"10.1016/j.ymssp.2025.112441","DOIUrl":"10.1016/j.ymssp.2025.112441","url":null,"abstract":"<div><div>This article proposes an adaptive control scheme for multi-flexible manipulators to address the challenges of unknown control directions and time-varying actuator faults, then realize contact force control and vibration suppression. Unlike existing methods that require identical control directions, the proposed approach expands application scope to multi-agent systems with nonidentical control directions. By introducing extended Nussbaum functions with saturated amplitudes, the scheme prevents oscillations caused by the unbounded growth of traditional Nussbaum functions. The interdependent design of multiple extended Nussbaum functions enhances the robustness against actuator faults and unknown control dynamics. The controllers achieve consensus in contact force control while suppressing vibrations, ensuring global stability and asymptotic convergence without relying on force sensors, thus avoiding sensor noise. Simulation results demonstrate the effectiveness of the proposed approach in dealing with the unknown control dynamics and realizing control objectives. The theoretical analysis and simulation results demonstrate that the proposed approach is a promising solution for practical multi-agent applications including force control of multi-flexible manipulators.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112441"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418572","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 : 2025-02-16DOI: 10.1016/j.ymssp.2025.112393
Ran Wang , Rujie Ji , Liang Yu , Weikang Jiang
Acoustic array measurements are widely employed for noise analysis, noise control, and consequently for low-noise design of product. In the presence of rotating machinery (e.g., rotors), acoustic signals typically include a mix of tonal and broadband components as well as background noise. The broadband components characterized by their periodic modulation can be effectively modeled as a second-order cyclostationary (CS2) signal. In recent years, the extraction of tonal components from acoustic array measurements has been extensively studied by many researchers. However, the extraction of the CS2 components from the acoustic array measurements presents a significant challenge, especially in wind tunnel tests. This paper presents a novel approach that constructs a time-varying periodic variance model to characterize the CS2 signal and a time-invariant variance model to characterize the background noise to address this issue. The distribution of the parameters in the model is estimated using variational Bayesian (VB) inference to construct a time-varying periodic filter. Importantly, a special processing in this paper is employed to enable the simultaneous extraction of the CS2 signal from multi-channel acoustic array measurements. The proposed method is evaluated through extensive simulations. Finally, the efficiency and applicability of the proposed method are validated through a helicopter rotor model and a twin-rotor helicopter model in wind tunnel tests.
{"title":"Extraction of second-order cyclostationary signal from acoustic array measurements using a time-varying periodic variance model with variational Bayesian inference","authors":"Ran Wang , Rujie Ji , Liang Yu , Weikang Jiang","doi":"10.1016/j.ymssp.2025.112393","DOIUrl":"10.1016/j.ymssp.2025.112393","url":null,"abstract":"<div><div>Acoustic array measurements are widely employed for noise analysis, noise control, and consequently for low-noise design of product. In the presence of rotating machinery (e.g., rotors), acoustic signals typically include a mix of tonal and broadband components as well as background noise. The broadband components characterized by their periodic modulation can be effectively modeled as a second-order cyclostationary (CS2) signal. In recent years, the extraction of tonal components from acoustic array measurements has been extensively studied by many researchers. However, the extraction of the CS2 components from the acoustic array measurements presents a significant challenge, especially in wind tunnel tests. This paper presents a novel approach that constructs a time-varying periodic variance model to characterize the CS2 signal and a time-invariant variance model to characterize the background noise to address this issue. The distribution of the parameters in the model is estimated using variational Bayesian (VB) inference to construct a time-varying periodic filter. Importantly, a special processing in this paper is employed to enable the simultaneous extraction of the CS2 signal from multi-channel acoustic array measurements. The proposed method is evaluated through extensive simulations. Finally, the efficiency and applicability of the proposed method are validated through a helicopter rotor model and a twin-rotor helicopter model in wind tunnel tests.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112393"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418573","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 : 2025-02-16DOI: 10.1016/j.ymssp.2025.112451
Zhongxi Zhu , Hong Liu , Wanneng Lei , Youqiang Xue , Changjian Xiao
The occurrence of leakage in the wellbore annulus can severely impact drilling operations. To address well leakage detection, an improved diagnostic model is proposed, optimising three aspects: signal processing, feature selection, and model optimisation. The model combines the RIME-ICEEMDAN-SWT signal processing method with SAPSO-GWO-LSSVM for diagnosis. First, the Rime Optimization Algorithm (RIME) optimises the Improved Complete Ensemble Empirical Mode Decomposition with Additive Noise (ICEEMDAN), enhancing the algorithm’s self-adaptive tuning capability. Simulation and experimental results demonstrate that the RIME-ICEEMDAN-SWT method improves the quality of well leakage signals. To better extract leakage features, time–frequency information from the signal is fully integrated, and Lasso regression is used for feature dimensionality reduction, enabling automatic selection of key features. Finally, to reduce model complexity, a SAPSO-GWO-LSSVM-based diagnostic model is developed, integrating Simulated Annealing (SA), Particle Swarm Optimisation (PSO), and Grey Wolf Optimisation (GWO) to form a hybrid population intelligent optimisation algorithm. This algorithm optimises the Least Squares Support Vector Machine (LSSVM). The model is tested on five simulated wellbores of various sizes, achieving an average diagnostic accuracy above 95%, with a standard deviation between 0.35 and 0.6. The results confirm the model’s high accuracy and stability.
{"title":"Optimising wellbore annular leakage detection and diagnosis model: A signal feature enhancement and hybrid intelligent optimised LSSVM approach","authors":"Zhongxi Zhu , Hong Liu , Wanneng Lei , Youqiang Xue , Changjian Xiao","doi":"10.1016/j.ymssp.2025.112451","DOIUrl":"10.1016/j.ymssp.2025.112451","url":null,"abstract":"<div><div>The occurrence of leakage in the wellbore annulus can severely impact drilling operations. To address well leakage detection, an improved diagnostic model is proposed, optimising three aspects: signal processing, feature selection, and model optimisation. The model combines the RIME-ICEEMDAN-SWT signal processing method with SAPSO-GWO-LSSVM for diagnosis. First, the Rime Optimization Algorithm (RIME) optimises the Improved Complete Ensemble Empirical Mode Decomposition with Additive Noise (ICEEMDAN), enhancing the algorithm’s self-adaptive tuning capability. Simulation and experimental results demonstrate that the RIME-ICEEMDAN-SWT method improves the quality of well leakage signals. To better extract leakage features, time–frequency information from the signal is fully integrated, and Lasso regression is used for feature dimensionality reduction, enabling automatic selection of key features. Finally, to reduce model complexity, a SAPSO-GWO-LSSVM-based diagnostic model is developed, integrating Simulated Annealing (SA), Particle Swarm Optimisation (PSO), and Grey Wolf Optimisation (GWO) to form a hybrid population intelligent optimisation algorithm. This algorithm optimises the Least Squares Support Vector Machine (LSSVM). The model is tested on five simulated wellbores of various sizes, achieving an average diagnostic accuracy above 95%, with a standard deviation between 0.35 and 0.6. The results confirm the model’s high accuracy and stability.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112451"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418569","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}
An adaptive active vibration control approach has been proposed for flexible beam systems to reject unknown deterministic disturbance in this paper. In the proposed feedback control scheme, a robust controller is firstly developed to stabilize the mode-truncated inner-loop system with the properly formulated uncertain transfer functions. Subsequently, the Youla parameters are augmented with the base robust controller to formulate a Q parametrized set of all stabilizing controllers, whose dimensionality of Q is suitably selected to meet the requirement pertaining to the robustness in the context of model uncertainties of the flexible beam system and the disturbance characteristics. A recursive least squares (RLS) algorithm incorporating projection is utilized to adjust the augmented Youla parameters online for the disturbance with the unknown and time-varying characteristics. The existence of Youla parameters and the stability of the proposed Youla adaptive vibration control scheme have been analyzed. The simulation for a flexible beam system against unknown deterministic disturbance and an experimental test evaluation to attenuate the unknown flying height fluctuations of the read/write head suspension in data storage system have been illustrated to show the effectiveness of the proposed adaptive active vibration control approach.
{"title":"An adaptive active vibration control for flexible beam systems under unknown deterministic disturbances","authors":"Fanfan Qian , Haichun Ding , Tianqi Liu , Zhizheng Wu , Xuping Zhang , Azhar Iqbal","doi":"10.1016/j.ymssp.2025.112447","DOIUrl":"10.1016/j.ymssp.2025.112447","url":null,"abstract":"<div><div>An adaptive active vibration control approach has been proposed for flexible beam systems to reject unknown deterministic disturbance in this paper. In the proposed feedback control scheme, a robust controller is firstly developed to stabilize the mode-truncated inner-loop system with the properly formulated uncertain transfer functions. Subsequently, the Youla parameters are augmented with the base robust controller to formulate a <em>Q</em> parametrized set of all stabilizing controllers, whose dimensionality of <em>Q</em> is suitably selected to meet the requirement pertaining to the robustness in the context of model uncertainties of the flexible beam system and the disturbance characteristics. A recursive least squares (RLS) algorithm incorporating projection is utilized to adjust the augmented Youla parameters online for the disturbance with the unknown and time-varying characteristics. The existence of Youla parameters and the stability of the proposed Youla adaptive vibration control scheme have been analyzed. The simulation for a flexible beam system against unknown deterministic disturbance and an experimental test evaluation to attenuate the unknown flying height fluctuations of the read/write head suspension in data storage system have been illustrated to show the effectiveness of the proposed adaptive active vibration control approach.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112447"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418570","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 : 2025-02-16DOI: 10.1016/j.ymssp.2025.112454
Yangjun Wu , Hongliang Yao , Xuangong Li , Shendong Han
To suppress the torsional vibration of shafts, a metamaterial structure made up of the lattice structure with a low Poisson’s ratio is presented in this paper. Firstly, to improve the efficiency, the homogeneous model is established to replace the lattice structure. Its homogenized properties are estimated using the asymptotic homogenization (AH) method. Subsequently, through the Bragg scattering (BS) mechanism and the transfer matrix method (TMM), a parametric analysis is performed to evaluate the influence of the structural parameters of the metamaterial shaft on the band-gap distribution. Furthermore, using the data-driven optimization method, the band-gap distribution of the metamaterial shaft is optimized so as to attenuate the torsional vibration of the electric drive system of the hybrid electric vehicle (HEV). The optimization results show that not only does the range of band gap for the optimized metamaterial shaft satisfy the optimization target of 1200–4000 Hz, but also the band-gap width increases significantly. Finally, the optimized metamaterial shaft is fabricated by additive manufacturing technique, and its vibration isolation performance is verified experimentally.
{"title":"Metamaterial shaft with a low Poisson’s ratio lattice structure for torsional vibration isolation","authors":"Yangjun Wu , Hongliang Yao , Xuangong Li , Shendong Han","doi":"10.1016/j.ymssp.2025.112454","DOIUrl":"10.1016/j.ymssp.2025.112454","url":null,"abstract":"<div><div>To suppress the torsional vibration of shafts, a metamaterial structure made up of the lattice structure with a low Poisson’s ratio is presented in this paper. Firstly, to improve the efficiency, the homogeneous model is established to replace the lattice structure. Its homogenized properties are estimated using the asymptotic homogenization (AH) method. Subsequently, through the Bragg scattering (BS) mechanism and the transfer matrix method (TMM), a parametric analysis is performed to evaluate the influence of the structural parameters of the metamaterial shaft on the band-gap distribution. Furthermore, using the data-driven optimization method, the band-gap distribution of the metamaterial shaft is optimized so as to attenuate the torsional vibration of the electric drive system of the hybrid electric vehicle (HEV). The optimization results show that not only does the range of band gap for the optimized metamaterial shaft satisfy the optimization target of 1200–4000 Hz, but also the band-gap width increases significantly. Finally, the optimized metamaterial shaft is fabricated by additive manufacturing technique, and its vibration isolation performance is verified experimentally.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112454"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418568","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 : 2025-02-16DOI: 10.1016/j.ymssp.2025.112419
Peng Huang , Zhongcai Zhang , Yang Gao
In practical industrial transportation applications, tower cranes play a crucial role. However, the existing control methods for tower cranes often overlook the double-pendulum effect or only consider the linearized double-pendulum dynamic model. To address these issues, for double-pendulum tower cranes, this paper proposes an amplitude-saturated nonlinear control method without velocity measurement. This design approach does not require any linearization operation and effectively eliminates the effect of steady-state error through elaborately constructed composite variables. The theoretical analysis guarantees that the controller can effectively suppress load oscillation and satisfy input constraints. Furthermore, it is proved that the state variables of the closed-loop system can asymptotically converge to the equilibrium point by means of Lyapunov theorem and LaSalle’s invariance principle. Finally, experimental results are provided to validate the feasibility of the proposed method.
{"title":"Amplitude-saturated control of underactuated double-pendulum tower cranes: Design and experiments","authors":"Peng Huang , Zhongcai Zhang , Yang Gao","doi":"10.1016/j.ymssp.2025.112419","DOIUrl":"10.1016/j.ymssp.2025.112419","url":null,"abstract":"<div><div>In practical industrial transportation applications, tower cranes play a crucial role. However, the existing control methods for tower cranes often overlook the double-pendulum effect or only consider the linearized double-pendulum dynamic model. To address these issues, for double-pendulum tower cranes, this paper proposes an amplitude-saturated nonlinear control method without velocity measurement. This design approach does not require any linearization operation and effectively eliminates the effect of steady-state error through elaborately constructed composite variables. The theoretical analysis guarantees that the controller can effectively suppress load oscillation and satisfy input constraints. Furthermore, it is proved that the state variables of the closed-loop system can asymptotically converge to the equilibrium point by means of Lyapunov theorem and LaSalle’s invariance principle. Finally, experimental results are provided to validate the feasibility of the proposed method.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112419"},"PeriodicalIF":7.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418571","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 : 2025-02-15DOI: 10.1016/j.ymssp.2025.112434
M. Palmieri , J. Slavič , F. Cianetti
In vibration fatigue analysis, spectral methods are used to evaluate the fatigue damage of structures experiencing random vibrations. Spectral methods fail under non-Gaussian and non-stationary loading conditions and various solutions have been proposed. Correction coefficients are promising and depend on the kurtosis and skewness of the system’s response, which requires extensive time-domain analyses. Performing time-domain analysis undermines the computational efficiency of spectral methods. The present manuscript proposes a modal decomposition-based approach to numerically efficiently compute the central moments required to obtain the kurtosis and skewness. The proposed method is numerically validated on a structure subjected to non-Gaussian random loads. The proposed method demonstrates results identical to the standard approach, showing a reduction in computation time of around two orders of magnitude. This extends the applicability of spectral methods in conjunction with correction coefficients for numerical estimation of fatigue damage in the frequency domain even in the case of non-Gaussian loadings.
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