Mechanical Systems and Signal Processing最新文献

{"title":"A multi-axial Fatigue Damage Spectrum for the evaluation of the fatigue damage potential of multi-axis random vibration environments","authors":"Enrico Proner,&nbsp;Emiliano Mucchi","doi":"10.1016/j.ymssp.2025.112362","DOIUrl":"10.1016/j.ymssp.2025.112362","url":null,"abstract":"<div><div>The Fatigue Damage Spectrum (FDS) method has been a cornerstone in the characterization and evaluation of vibrational loads. However, the formulation of the FDS requires to simplify the Device Under Test (DUT) as a Single-Input-Single-Output system, while real components are, in general, Multi-Input Multi-Output (MIMO) systems. This strict assumption neglects the possibility of multi-axis testing. In MIMO random tests, one critical aspect is the phase and coherence between each pair of single-axis excitation. Nevertheless, the FDS is still used to define test-tailored specifications. In this context, this paper proposes an improved version of the traditional FDS to be used to classify multi-axis random vibration environments. In particular, the proposed approach improves the traditional formulation of the FDS by incorporating the correlation of multi-axial vibration environments, enabling a better evaluation of the fatigue damage potential of multi-axis random vibration environments. This paper provides the theoretical formulation of the proposed methodology as well as its experimental verification. The experimental verification showcases the capability of the method to evaluate the damage potential of different multi-axis and single-axis vibration environments. In the experiments, the MI-FDS has been able to identify the damage potential of different vibration environments and allow to synthesize a new damage equivalent vibration environment. The evaluation of the MI-FDS aligns with the actual time to failure of the tested specimens.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112362"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035254","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":"Corrigendum to “An improved EnlightenGAN shadow removal framework for images of cracked concrete” [Mech. Syst. Signal Process 223 (2025) 111943]","authors":"Rui Sun ,&nbsp;Xuming Li ,&nbsp;Siu-Seong Law ,&nbsp;Libing Zhang ,&nbsp;Lingzhi Hu ,&nbsp;Gang Liu","doi":"10.1016/j.ymssp.2025.112385","DOIUrl":"10.1016/j.ymssp.2025.112385","url":null,"abstract":"","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112385"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic modelling of a floating spline-coupling shaft system with parallel misalignment and tooth backlash","authors":"Xinbin Li ,&nbsp;Yajun Xu ,&nbsp;Jing Liu ,&nbsp;Jianyu Liu ,&nbsp;Guang Pan ,&nbsp;Zhifeng Shi","doi":"10.1016/j.ymssp.2025.112363","DOIUrl":"10.1016/j.ymssp.2025.112363","url":null,"abstract":"<div><div>Floating splines are widely used in various shaft systems with high installation difficulty. The spline couplings’ stiffness characteristics significantly affect the shaft system dynamics. Most previous works have focused on ordinary spline-coupling shaft system dynamic modeling and ignored the effect of internal spline fillet-foundation deflection on the spline coupling stiffness. Thus, an improved stiffness calculation method of spline coupling considering internal spline fillet-foundation deflection is proposed in this work. The meshing force considering parallel misalignment and tooth backlash, and the parallel misalignment force are given. Then, the floating spline-coupling shaft system dynamic model with parallel misalignment and tooth backlash is established. An experiment is conducted to validate the accuracy of the proposed floating spline-coupling shaft system dynamic model. The results obtained from the proposed method and finite element (FE) models are compared. Moreover, the results obtained using the proposed method and other works are compared. Finally, the effects of parallel misalignment on the spline meshing force and spline coupling-shaft system dynamics are investigated. This work helps in accurate floating spline-coupling shaft system dynamic modeling and can provide guidance for diagnosing parallel misalignment error.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112363"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel enhanced sensitive sensor for small-diameter pipe based on the PECT-EMAT hybrid testing method","authors":"Shuyan Yang ,&nbsp;Ruixiang Zhao ,&nbsp;Zhirong Duan ,&nbsp;Peng Hu ,&nbsp;Zhenmao Chen ,&nbsp;Shejuan Xie ,&nbsp;Qiang Wan ,&nbsp;Shunping Yan ,&nbsp;Yang Zheng","doi":"10.1016/j.ymssp.2025.112350","DOIUrl":"10.1016/j.ymssp.2025.112350","url":null,"abstract":"<div><div>Metal pipelines are widely used in oil, natural gas and other industries. Due to their long-term relatively harsh service conditions, various complex defects will inevitably occur both on the inner and outer wall. Conventional single non-destructive testing technology cannot detect both inner and outer wall defects simultaneously. A hybrid non-destructive testing technology combining pulsed eddy current testing and electromagnetic acoustic transducer (PECT-EMAT) is a good candidate to effectively solve this problem. However, the current research object based on this hybrid testing method is mainly for the flat plate or the large-diameter pipe similar to the flat plate. The increased curvature of small-diameter pipe will decrease the detection performance using traditional rigid sensing module due to the interface in-adaptation. Based on above background, this study proposes three different sensing configurations for curved pipe and their detection performance are investigated and compared. Firstly, the simulation model is built for the pipeline. Then, the near-surface eddy current fields excited by the planar and curved coils, the bias magnetic fields provided by the planar and curved magnets, and the Lorentz force excited by three types of sensors with different configurations are calculated and compared, respectively. Finally, the detection performance of the three different sensor configurations are compared through simulation and experiment. The results show that the designed novel composite sensor has the highest PECT sensitivity and the largest EMAT amplitude and SNR, which contribute to higher detection capability. To some extent, the proposed composite sensor can be employed to solve the problem of detection performance decreasing due to the increased curvature of small-diameter pipe.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112350"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035258","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":"Large-scale wind turbine blade operational condition monitoring based on UAV and improved YOLOv5 deep learning model","authors":"Wanrun Li ,&nbsp;Wenhai Zhao ,&nbsp;Yongfeng Du","doi":"10.1016/j.ymssp.2025.112386","DOIUrl":"10.1016/j.ymssp.2025.112386","url":null,"abstract":"<div><div>Monitoring the operational condition of wind turbine blades is a critical aspect of structural health monitoring, especially considering the challenges associated with traditional sensing techniques on rotating blades. This paper proposes an innovative method for monitoring large-scale wind turbine blades using an improved YOLOv5 (You Only Look Once) deep learning model that eliminates the use of manual markers. Firstly, the SE (Squeeze and Excitation) attention mechanism is added to the original YOLOv5 deep learning model to enhance the features of the target, while the pre-training model and freeze training strategy of migration learning are added to improve the model training speed and convergence efficiency. Additionally, a pre-trained model from transfer learning is integrated, along with a freeze training strategy, to expedite the training process and improve convergence efficiency. Secondly, the Deep sort algorithm is integrated seamlessly as a tracking mechanism to encode and track the targets detected by the improved YOLOv5 model. This enables the classification and coordinate output of selected targets across multiple blades, providing a comprehensive understanding of their operational condition. To validate the performance of the proposed SE_Tfreeze_YOLOv5 deep learning model, rigorous testing and assessments are conducted. The training loss, accuracy, and time of the model were evaluated and compared to several other models to demonstrate the superiority of the model. Laboratory tests were used to validate blade operating patterns at different rotational speeds, and the relationships between blade trajectories, spacing, and time–frequency information during blade operation were thoroughly discussed. To validate the practicality and reliability of the method, field monitoring measurements are performed on two 2 MW wind turbines located in a wind farm in western China. The monitoring demonstrates the capability of the vision method for remote, low-cost, high-precision, multi-point monitoring of wind turbine blades under operational conditions. The results of these monitoring are encouraging and indicate the potential of this approach for widespread application in the wind energy industry.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112386"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid deep learning-based approach for on-line chatter detection in milling using deep stem-inception networks and residual channel-spatial attention mechanisms","authors":"Khairul Jauhari ,&nbsp;Achmad Zaki Rahman ,&nbsp;Mahfudz Al Huda ,&nbsp;Achmad Widodo ,&nbsp;Toni Prahasto","doi":"10.1016/j.ymssp.2025.112357","DOIUrl":"10.1016/j.ymssp.2025.112357","url":null,"abstract":"<div><div>Excessive chatter significantly degrades both the quality of the finished workpiece surface and the efficiency of machining operations. To address this productivity bottleneck, online chatter detection has emerged as a key area of research in recent years. However, existing approaches often rely on manually extracted features, which can limit their effectiveness. Deep learning, with its automatic feature extraction and feature learning capabilities, presents a promising alternative for more general and accurate detection, but its effectiveness relies on well-labeled training data, which remains a challenge. Therefore, this study introduces a novel hybrid deep convolution neural network (CNN) architecture that combines the stem block and the Inception modules with the channel-spatial attention mechanism embedded in the residual network block (RCS-block). It is called SIRCS-CNN. The stem-block serves as a crucial bridge between the raw input image and the deeper layers of a CNN, playing a vital role in extracting meaningful features and preparing the data for further processing by the deeper layers. To enhance the depth of the feature maps, the multi-scale features of the cutting vibration signal are automatically extracted by the two Inception sequential blocks. The RCS-block assigned focuses on capturing inter-channel and spatial dependencies by computing the attention weights across different channels and different spatial locations of the feature maps; therefore, it helps the network to emphasize important channels, suppress less relevant ones, and enhance model accuracy. Furthermore, the introduction of RCS-blocks also contributes to mitigating the risk of vanishing gradients and accelerating network training. Importantly, the combined strengths of these modules in SIRCS-CNN enable robust generalization and accurate performance by including transition state data in the training process. Experimental validation with a stepped-shaped workpiece under diverse machining parameters demonstrates the effectiveness of SIRCS-CNN in chatter detection. By obtaining classification accuracy of 100% on the validation set and 98.81% on the testing set, respectively, the results showed that the proposed model succeeds better than other models. The proposed model can accurately detect every machining state, including the transition phases, when compared to the existing methods. In addition, the proposed model recognizes the severe chatter earlier than other approaches, which is advantageous for suppressing the chatter.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112357"},"PeriodicalIF":7.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035257","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":"Frequency slice graph spectrum model and its application in bearing fault feature extraction","authors":"Kun Zhang ,&nbsp;Yanlei Liu ,&nbsp;Long Zhang ,&nbsp;Chaoyong Ma ,&nbsp;Yonggang Xu","doi":"10.1016/j.ymssp.2025.112383","DOIUrl":"10.1016/j.ymssp.2025.112383","url":null,"abstract":"<div><div>Complex electrical equipment in harsh environments can pose a major threat to the health of key components such as bearings. Weak features are often hidden in many interferences, which makes it very difficult to extract fault features of mechanical parts. This paper proposes a frequency slice graph spectrum model (FSGS Model), which aims to search for characteristic information that matches bearing faults from the enhanced data dimension. Firstly, the frequency slice groups in the time–frequency domain are used as graph structure vertices to construct a Laplacian matrix. On the basis of retaining the time domain features, the connection between the fault features in the spectrum is mined. Secondly, time–frequency graph Fourier clustering spectrum is established. The order of the clustering spectrum is tower-decomposed and reconstructed through a binary tree structure, providing different order combinations. In order to increase the recognition accuracy, the harmonic spectral kurtosis (HSK) is used to select the optimal reconstructed FSGS spectrum band. The feasibility of the proposed method is verified by constructing simulation signals, and it is applied to the fault diagnosis of the inner and outer rings of bearings. The effectiveness of this method is verified by comparison with three methods of Fast Kurtogram, Autogram, and Harmogram.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112383"},"PeriodicalIF":7.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035259","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":"Nonlinear vibration control of high-aspect-ratio composite trapezoidal plate wing structure via shape memory alloy: Theoretical formulation and experimental research","authors":"Ze-Yu Chai ,&nbsp;Xu-Yuan Song ,&nbsp;Ye-Wei Zhang ,&nbsp;Li-Qun Chen","doi":"10.1016/j.ymssp.2025.112360","DOIUrl":"10.1016/j.ymssp.2025.112360","url":null,"abstract":"<div><div>It is a crucial mission for the engineers to ensure the dynamic stability and safety of the composite wing with high-aspect-ratio, which is the effective component to guarantee aerodynamic efficiency of aircraft. This contribution addresses on a systematic theoretical and experimental investigation on the vibration control of the high-aspect-ratio composite wing of electric aircraft which is simplified to the composite laminated trapezoidal plate (CLTP). The shape memory alloy (SMA), which belongs to the light high-damping material with great energy absorbing characteristics, is imported for the nonlinear vibration control of high-aspect-ratio wing. The governing equation of the trapezoidal plate in the hygrothermal environment is obtained through the coordinate change in frame of the Kirchhoff thin plate theory. On the basis of determining the constitutive equation and the ability of energy dissipation at various temperatures of shape memory alloy via experiment and theory, the dynamic equation of the composite laminated trapezoidal plate with SMA is established via a beam- trapezoidal plate combing structure, in which the SMA is stand by the Euler beam with nonlinear constitutive model. After confirming the accuracy of present formulation, the vibration damping ability of shape memory alloy on CLTP is demonstrated. The influence of temperature, laying mode and external excitation amplitude on the vibration suppression performance of shape memory alloy is revealed. Some novel conclusions are reached, which can provide theoretical and experimental guidance for the dynamic and vibration suppression of the high-aspect-ratio composite wing of electric aircraft.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112360"},"PeriodicalIF":7.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035335","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":"Energy-saving tracking control and experiment of nonlinear active suspension for multi-axle vehicles considering road slope","authors":"Wenbin Liu ,&nbsp;Mingde Gong ,&nbsp;Hao Chen ,&nbsp;Dingxuan Zhao","doi":"10.1016/j.ymssp.2025.112328","DOIUrl":"10.1016/j.ymssp.2025.112328","url":null,"abstract":"<div><div>This article proposes a novel active suspension model of a three-axle heavy vehicle considering road slope and designs an energy-saving backstepping tracking control method based on an ideal reference model and disturbance observer. The motion states of the ideal reference model with sky-hook damping force control are designed as the tracking trajectory of active suspension control. Then the nonlinear extended state observer (NLESO) is designed to estimate the unavoidable internal parameter perturbations and external unknown disturbances in nonlinear suspension systems. In particular, different from the existing control methods, the influence of road slope is considered in the suspension system modeling for the first time, which can reflect the motion states of the car body more accurately. The tracking controller makes full use of the beneficial nonlinear dynamic characteristics of the ideal reference model, which can effectively ensure ride comfort and consume less energy. The complete stability proof of the control system is given, which establishes the theoretical basis for the active suspension to adapt to the new road conditions. The results of simulation and real vehicle tests under various road conditions (especially slope unilateral convex hull road) show that the proposed controller has better shock absorption performance and lower energy consumption compared with passive suspension and the existing controllers.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112328"},"PeriodicalIF":7.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035339","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":"Towards a single-loop Gaussian process regression based-active learning method for time-dependent reliability analysis","authors":"Chao Dang,&nbsp;Marcos A. Valdebenito,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.ymssp.2024.112294","DOIUrl":"10.1016/j.ymssp.2024.112294","url":null,"abstract":"<div><div>Time-dependent reliability analysis has received increasing attention for assessing the performance and safety of engineered components and systems subject to both random and time-varying dynamic factors. However, many existing methods may prove insufficient when applied to real-world problems, particularly in terms of applicability, efficiency and accuracy. This paper presents a novel time-dependent reliability analysis method called ‘single-loop Gaussian process regression based-active learning’ (SL-GPR-AL). In this method, a GPR model is trained as a global response surrogate model for the time-dependent performance function in an active learning fashion. A new stopping criterion is proposed to assess the convergence of the GPR model in estimating the time-dependent failure probability. Additionally, two new learning functions are introduced to identify the best next point for further refining the GPR model if the stopping criterion is not met. Finally, the well-trained GPR model in conjunction with Monte Carlo simulation provides the time-dependent failure probability over a specified time interval, along with the time-dependent failure probability function as a byproduct. Four numerical examples are analyzed to demonstrate the performance of the proposed method. The results indicate that our approach provides an alternative, efficient and accurate means for computationally expensive time-dependent reliability analysis.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"226 ","pages":"Article 112294"},"PeriodicalIF":7.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035341","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}