This study develops a simplified model incorporating an over-height vehicle and the height restriction frame (HRF) to explore the failure modes and mechanical properties of the HRF when subjected to an impact from the over-height vehicle. Within the study context, rigorous model tests have been constructed for simulation analysis. The validity of these numerical simulations is confirmed by comparing the test results to the calculated outcomes. The study also analyzes the dynamic response of vehicles varying in speed and weight when impacting the HRF. The findings reveal that most of the beam's displacement can be attributed to the column's overturning, while a lesser portion is due to the plastic deformation of the beam. The column's displacement is primarily caused by its own overturning. Both the beam and the column's base demonstrate evidence of elastoplastic deformation. It is observed that the displacement and stress of crucial nodes rise with the increase in vehicle speed and weight. Vehicle speed emerges as the predominant factor influencing the impact force of the vehicle when compared to the vehicle's weight. Furthermore, the increase in vehicle weight extends the collision time between the vehicle and the HRF, indicating that the weight of the vehicle plays a significant role in the column's overturning. The study findings can potentially serve as both an experimental and theoretical reference for the design and calculation of the HRF.
{"title":"Model test and numerical analysis of height restriction frame to over-height vehicle impact","authors":"Yan Zhou, Zhushan Guo, Kai Zhang, Jinzhi Yi","doi":"10.21595/jve.2023.23324","DOIUrl":"https://doi.org/10.21595/jve.2023.23324","url":null,"abstract":"This study develops a simplified model incorporating an over-height vehicle and the height restriction frame (HRF) to explore the failure modes and mechanical properties of the HRF when subjected to an impact from the over-height vehicle. Within the study context, rigorous model tests have been constructed for simulation analysis. The validity of these numerical simulations is confirmed by comparing the test results to the calculated outcomes. The study also analyzes the dynamic response of vehicles varying in speed and weight when impacting the HRF. The findings reveal that most of the beam's displacement can be attributed to the column's overturning, while a lesser portion is due to the plastic deformation of the beam. The column's displacement is primarily caused by its own overturning. Both the beam and the column's base demonstrate evidence of elastoplastic deformation. It is observed that the displacement and stress of crucial nodes rise with the increase in vehicle speed and weight. Vehicle speed emerges as the predominant factor influencing the impact force of the vehicle when compared to the vehicle's weight. Furthermore, the increase in vehicle weight extends the collision time between the vehicle and the HRF, indicating that the weight of the vehicle plays a significant role in the column's overturning. The study findings can potentially serve as both an experimental and theoretical reference for the design and calculation of the HRF.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main focus of paper is to get full understanding of four different types of shock absorbers characteristics and their effects on the vertical evaluation indexes of a bus subjected to harmonic excitation. The quarter car with two degree of freedom (2DOF) model was employed to calculate the vertical evaluation indexes. The bus is assumed to travel at a constant velocity on a road surface with a profile following a sinusoidal function. The four types of the shock absorber are Linear Symmetric (LS), Nonlinear Symmetric (NS), Linear Asymmetric (LA), and Nonlinear Asymmetric (NA). As for the LS type, the damping force is a linear function of the relative velocity, and the damping force is symmetric for the conditions of the positive and negative relative velocities. Same as for the LA type, it means that the damping force is linear, however it is asymmetric and differentiated according to the suspension state of stroke (compression or extension). As for the NS type, the damping force function is symmetric, nonlinear, differentiated according to the magnitude of the relative velocity. As for the last NA type, the damping force function is also nonlinear and differentiated according to the magnitude of the relative velocity, but it is also asymmetric. The obtained evaluation indexes of the relative displacement of the bus suspension, acceleration of the bus body, and the dynamic tire load within the frequency range of 0 and 25 (Hz), the common frequency range of the bus in operations. The results suggest that the NA shock absorber type is more effective in reducing the suspension dynamic deflection stroke, improving the road holding and maintaining the ride comfort. The systematic assessments of the shock absorber characteristics should guide interested readers in selecting the most appropriate damping coefficient.
本文的重点是全面了解四种不同类型减震器的特性及其对受到谐波激励的公共汽车垂直评估指标的影响。本文采用具有两个自由度（2DOF）的四分之一车模型来计算垂直评估指标。假定公交车以恒定速度在路面上行驶，路面轮廓为正弦函数。减震器有四种类型：线性对称（LS）、非线性对称（NS）、线性非对称（LA）和非线性非对称（NA）。LS 型的阻尼力是相对速度的线性函数，阻尼力在相对速度为正和负的条件下是对称的。与 LA 型相同，这意味着阻尼力是线性的，但它是不对称的，并根据悬架的冲程状态（压缩或拉伸）而有所区别。至于 NS 型，阻尼力函数是对称的、非线性的，根据相对速度的大小而不同。至于最后一种 NA 型，阻尼力函数也是非线性的，并根据相对速度的大小进行微分，但也是不对称的。在客车运行的常用频率范围 0 和 25（Hz）内，得到了客车悬架相对位移、车身加速度和轮胎动载荷的评价指标。结果表明，NA 型减震器能更有效地减小悬架的动态变形行程，改善路面保持力并保持乘坐舒适性。对减震器特性的系统评估应能指导感兴趣的读者选择最合适的阻尼系数。
{"title":"Influence of a nonlinear asymmetric shock absorber on vibration of a bus subjected to harmonic excitation","authors":"Huu Nhan Tran, Fergyanto E Gunawan, Ngoc Dai Pham","doi":"10.21595/jve.2023.23404","DOIUrl":"https://doi.org/10.21595/jve.2023.23404","url":null,"abstract":"The main focus of paper is to get full understanding of four different types of shock absorbers characteristics and their effects on the vertical evaluation indexes of a bus subjected to harmonic excitation. The quarter car with two degree of freedom (2DOF) model was employed to calculate the vertical evaluation indexes. The bus is assumed to travel at a constant velocity on a road surface with a profile following a sinusoidal function. The four types of the shock absorber are Linear Symmetric (LS), Nonlinear Symmetric (NS), Linear Asymmetric (LA), and Nonlinear Asymmetric (NA). As for the LS type, the damping force is a linear function of the relative velocity, and the damping force is symmetric for the conditions of the positive and negative relative velocities. Same as for the LA type, it means that the damping force is linear, however it is asymmetric and differentiated according to the suspension state of stroke (compression or extension). As for the NS type, the damping force function is symmetric, nonlinear, differentiated according to the magnitude of the relative velocity. As for the last NA type, the damping force function is also nonlinear and differentiated according to the magnitude of the relative velocity, but it is also asymmetric. The obtained evaluation indexes of the relative displacement of the bus suspension, acceleration of the bus body, and the dynamic tire load within the frequency range of 0 and 25 (Hz), the common frequency range of the bus in operations. The results suggest that the NA shock absorber type is more effective in reducing the suspension dynamic deflection stroke, improving the road holding and maintaining the ride comfort. The systematic assessments of the shock absorber characteristics should guide interested readers in selecting the most appropriate damping coefficient.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.
针对车辆状态估计中遇到的异常数据处理问题，提出了一种多机学习改进型自适应卡尔曼滤波方法。首先，通过引入经遗传算法改进的 BP 神经网络（GA-BPNN）对无特征卡尔曼滤波（UKF）算法进行改进，以调节和修正 UKF 方法的全局误差。然后，应用反离群技术全面消除测量中的孤立离群和斑点离群，实现了对 GA-BPNN-UKF 的进一步改进，显著提高了滤波过程的鲁棒性。最后，通过仿真验证了所提出的新算法的有效性，并对其结果进行了分析，为进一步的实际应用提供了可靠的依据。仿真结果表明，GA-BPNN 算法的估计性能明显优于扩展卡尔曼滤波（EKF）方法。
{"title":"Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method","authors":"Yingjie Liu, Dawei Cui, Wen Peng","doi":"10.21595/jve.2023.23441","DOIUrl":"https://doi.org/10.21595/jve.2023.23441","url":null,"abstract":"A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mechanical fault diagnosis can prevent the deterioration of mechanical equipment failures and is important for the stable operation of mechanical equipment. Firstly, this paper reviews three basic methods of fault diagnosis and common methods of data-driven fault diagnosis, focusing on the characteristics and advantages of deep learning and convolutional neural networks. Then, the basic structure and working principle of CNN (Convolutional Neural Networks) and some basic methods to achieve better training results are introduced. In the next place, from data processing, data fusion, sample set construction, and so on, it is reviewed that the method of fault diagnosis based on CNN and their application scenarios and advantages and disadvantages; for another, the related knowledge and concepts of transfer learning are introduced, and some current application scenarios and advantages and disadvantages of mechanical fault diagnosis techniques combining migration learning and convolutional neural networks are reviewed. Finally, the current difficulties and challenges of convolutional neural networks are discussed, and the research directions have been prospected for CNN applied to the field of fault diagnosis. Although there is quite some similar literature reviewed, this review aims to introduce the basic methods of fault diagnosis, which draw forth the basic applications of the fault diagnosis of data-driven, CNN in the domain of fault diagnosis, and the application scenarios and advantages and disadvantages of combining TL (Transfer Learning) and CNN in fault diagnosis, as well as some problems and prospects. It helps researchers to have a basic understanding of this.
{"title":"A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network","authors":"Junjian Hou, Xikang Lu, Yudong Zhong, Wenbin He, Dengfeng Zhao, Fang Zhou","doi":"10.21595/jve.2023.23391","DOIUrl":"https://doi.org/10.21595/jve.2023.23391","url":null,"abstract":"Mechanical fault diagnosis can prevent the deterioration of mechanical equipment failures and is important for the stable operation of mechanical equipment. Firstly, this paper reviews three basic methods of fault diagnosis and common methods of data-driven fault diagnosis, focusing on the characteristics and advantages of deep learning and convolutional neural networks. Then, the basic structure and working principle of CNN (Convolutional Neural Networks) and some basic methods to achieve better training results are introduced. In the next place, from data processing, data fusion, sample set construction, and so on, it is reviewed that the method of fault diagnosis based on CNN and their application scenarios and advantages and disadvantages; for another, the related knowledge and concepts of transfer learning are introduced, and some current application scenarios and advantages and disadvantages of mechanical fault diagnosis techniques combining migration learning and convolutional neural networks are reviewed. Finally, the current difficulties and challenges of convolutional neural networks are discussed, and the research directions have been prospected for CNN applied to the field of fault diagnosis. Although there is quite some similar literature reviewed, this review aims to introduce the basic methods of fault diagnosis, which draw forth the basic applications of the fault diagnosis of data-driven, CNN in the domain of fault diagnosis, and the application scenarios and advantages and disadvantages of combining TL (Transfer Learning) and CNN in fault diagnosis, as well as some problems and prospects. It helps researchers to have a basic understanding of this.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sleeve grouting connection stands as a customary method for interlinking precast shear walls within assembly construction. In the realm of on-site construction, achieving complete avoidance of sleeve grouting defects remains a challenge. In an endeavor to scrutinize the seismic performance and the subsequent progression of damage within shear wall structures riddled with sleeve grouting defects, a two-story shear wall model was formulated through the utilization of ABAQUS software. Employing numerical simulation of low cycle reciprocating loading, the study was conducted across three distinct operational contexts: absence of defects, localized defects, and comprehensive defects. The outcomes proffer insight into the exacerbated concrete damage triggered by defects present within shear wall structures. These defects contribute to premature yielding of reinforcement and a consequent amplification in the plastic length of the reinforcement, consequently impeding the harmonized deformation of reinforcement and concrete. The “pinch phenomenon” is particularly conspicuous within fully defective structures during the nascent loading stages. As cyclic loads mount, the hysteretic curves of both defective and defect-free structure tend to converge. While the skeleton curve of structures, whether grouting defects are present or not, demonstrates remarkable parity prior to reaching the pinnacle reaction force, the defective structure displays premature waning in reaction force. This, in turn, curtails the efficacy of early warning concerning structural deformation and jeopardizes safety. In light of the foregoing analysis, it is manifest that the presence of sleeve grouting defects significantly impacts the seismic performance and subsequent damage trajectory of shear wall structures. As a corollary, addressing and mitigating these defects during on-site construction emerge as imperative prerequisites for upholding the comprehensive safety and stability of the structure.
{"title":"Seismic performance of fabricated shear wall structures with design defects","authors":"Hua Yan, Bo Song, Huanhuan Yin","doi":"10.21595/jve.2023.23508","DOIUrl":"https://doi.org/10.21595/jve.2023.23508","url":null,"abstract":"The sleeve grouting connection stands as a customary method for interlinking precast shear walls within assembly construction. In the realm of on-site construction, achieving complete avoidance of sleeve grouting defects remains a challenge. In an endeavor to scrutinize the seismic performance and the subsequent progression of damage within shear wall structures riddled with sleeve grouting defects, a two-story shear wall model was formulated through the utilization of ABAQUS software. Employing numerical simulation of low cycle reciprocating loading, the study was conducted across three distinct operational contexts: absence of defects, localized defects, and comprehensive defects. The outcomes proffer insight into the exacerbated concrete damage triggered by defects present within shear wall structures. These defects contribute to premature yielding of reinforcement and a consequent amplification in the plastic length of the reinforcement, consequently impeding the harmonized deformation of reinforcement and concrete. The “pinch phenomenon” is particularly conspicuous within fully defective structures during the nascent loading stages. As cyclic loads mount, the hysteretic curves of both defective and defect-free structure tend to converge. While the skeleton curve of structures, whether grouting defects are present or not, demonstrates remarkable parity prior to reaching the pinnacle reaction force, the defective structure displays premature waning in reaction force. This, in turn, curtails the efficacy of early warning concerning structural deformation and jeopardizes safety. In light of the foregoing analysis, it is manifest that the presence of sleeve grouting defects significantly impacts the seismic performance and subsequent damage trajectory of shear wall structures. As a corollary, addressing and mitigating these defects during on-site construction emerge as imperative prerequisites for upholding the comprehensive safety and stability of the structure.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huakui Xu, Shaoping Yuan, Heng Luo, Kexin Wang, Pan Fang
The riser system acts as the vital link between the subsea blow-out preventer and the drilling platform. Affected by factors like top tension and marine environmental forces, the riser undergoes deformation and wear, carrying the risk of environmental pollution and financial losses upon failure. Hence, this study examines the riser's dynamic response to marine environmental loading. Initially, the motion differential equation for the riser system under the influence of nonlinear oceanic load is deduced using the principle of minimum potential energy and the variational method for extremum seeking. Subsequently, a nonlinear wave-current load model based on the Morrison equation is established, and the resulting equation is discretized into a finite element model using third-order Hermite interpolation function and the Galerkin weighted residual method. Finally, the dynamic response of the riser is scrutinized employing the Newmark numerical integration method. The study also investigates the impact of both oceanic environmental parameters and drilling parameters on the riser’s dynamic behavior. Comparative analysis of the numerical results reveals that the maximum displacement of the riser occurs at the middle section, whereas the maximum deflection angle is observed at the end of the riser. The periodicity of the deflection angle response is influenced by the position of the riser, showing a trend of decreasing and then increasing from the middle section towards the ends. Notably, the top tension and the velocity of the surface tidal current significantly affect the dynamic behavior of the riser. The findings of this study provide a theoretical foundation for the assessment of riser reliability and the determination of operational parameters.
{"title":"Dynamic behavior of risers under nonlinear oceanic environmental loading","authors":"Huakui Xu, Shaoping Yuan, Heng Luo, Kexin Wang, Pan Fang","doi":"10.21595/jve.2023.23497","DOIUrl":"https://doi.org/10.21595/jve.2023.23497","url":null,"abstract":"The riser system acts as the vital link between the subsea blow-out preventer and the drilling platform. Affected by factors like top tension and marine environmental forces, the riser undergoes deformation and wear, carrying the risk of environmental pollution and financial losses upon failure. Hence, this study examines the riser's dynamic response to marine environmental loading. Initially, the motion differential equation for the riser system under the influence of nonlinear oceanic load is deduced using the principle of minimum potential energy and the variational method for extremum seeking. Subsequently, a nonlinear wave-current load model based on the Morrison equation is established, and the resulting equation is discretized into a finite element model using third-order Hermite interpolation function and the Galerkin weighted residual method. Finally, the dynamic response of the riser is scrutinized employing the Newmark numerical integration method. The study also investigates the impact of both oceanic environmental parameters and drilling parameters on the riser’s dynamic behavior. Comparative analysis of the numerical results reveals that the maximum displacement of the riser occurs at the middle section, whereas the maximum deflection angle is observed at the end of the riser. The periodicity of the deflection angle response is influenced by the position of the riser, showing a trend of decreasing and then increasing from the middle section towards the ends. Notably, the top tension and the velocity of the surface tidal current significantly affect the dynamic behavior of the riser. The findings of this study provide a theoretical foundation for the assessment of riser reliability and the determination of operational parameters.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gear transmission system is an important component of the shearer cutting part. The quality of its performance affects the reliable and high-efficiency operation of the whole system. Multi-rigid body and rigid-flexible coupling models were established respectively, and the dynamic analysis is carried out in the virtual simulation software Adams for the gear transmission system of the shearer cutting section. The dynamic characteristics of the gear transmission system under different load conditions were studied. The effects of constant load torque and step load torque on the dynamic characteristics of the drive system are explored. The research results show that the simulation results obtained from the rigid-flexible coupling model of the gear transmission system are closer to the actual operating conditions. It provides a visual means of dynamic analysis, which is more intuitive and convenient. The research methods and results can provide a reference for the further exploration of the electromechanical coupling dynamic characteristics of the motor-gear transmission system.
{"title":"Dynamic characteristics analysis for gear transmission system in shearer cutting section under different loads","authors":"Guo Ye, Xing Deng, Jinyong Ju, Lianchao Sheng","doi":"10.21595/jve.2023.23478","DOIUrl":"https://doi.org/10.21595/jve.2023.23478","url":null,"abstract":"Gear transmission system is an important component of the shearer cutting part. The quality of its performance affects the reliable and high-efficiency operation of the whole system. Multi-rigid body and rigid-flexible coupling models were established respectively, and the dynamic analysis is carried out in the virtual simulation software Adams for the gear transmission system of the shearer cutting section. The dynamic characteristics of the gear transmission system under different load conditions were studied. The effects of constant load torque and step load torque on the dynamic characteristics of the drive system are explored. The research results show that the simulation results obtained from the rigid-flexible coupling model of the gear transmission system are closer to the actual operating conditions. It provides a visual means of dynamic analysis, which is more intuitive and convenient. The research methods and results can provide a reference for the further exploration of the electromechanical coupling dynamic characteristics of the motor-gear transmission system.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.
{"title":"Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)","authors":"Jingjing Gao, Jing Liu, Xinli Yu","doi":"10.21595/jve.2023.23361","DOIUrl":"https://doi.org/10.21595/jve.2023.23361","url":null,"abstract":"In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For mechanical metamaterials and their vibration isolation ability, a new corset type structure (CTS) is designed from the inward hexagonal steel structure by applying fillet at the inward corners. Ten CTS cells are born by using the different fillet radius. The fillet radius is 10 mm to 100 mm, but the cell mass remains constant when the plate has the same thickness. The static deformation, vibration modality and harmonic response of these NPR structures are analyzed in this paper. These CTS cells are modeled by using the finite element method (FEM) with a uniform grids. In static analysis, a surface load and a point load on the top plate are respectively considered to study the elastic deformation, the NPR and the stiffness of CTS cells with different fillet radii and thicknesses. These CTS cells have a greater NPR and a higher stiffness than the original inward hexagonal steel structure. In modal analysis, the natural frequency, the eigenmode and the fixed modality are numerically computed. These frequency values and displacement distributions of CTS cells show that these CTS cells have a higher vibration frequency than the origin inward hexagonal structure cell. In harmonic response analysis, the frequency domain is from 1 Hz to 1000 Hz, and the excitation force is on the top surface of the upper plate. All displacement responses of these CTS cells are analyzed. The harmonic response analysis result shows that the resonance magnitude can be significantly suppressed by these new CTS cells. The analysis result presents the characteristics of this new CTS, and it is beneficial for the vibration isolation in engineering application.
{"title":"Mechanical characteristics of a corset type structure with negative Poisson’s ratio","authors":"Yuchao Song, Yanxin Yang, Changkuan Chi, Guobin Li, Jiahui Zhang, Zhaowen Zhang","doi":"10.21595/jve.2023.23413","DOIUrl":"https://doi.org/10.21595/jve.2023.23413","url":null,"abstract":"For mechanical metamaterials and their vibration isolation ability, a new corset type structure (CTS) is designed from the inward hexagonal steel structure by applying fillet at the inward corners. Ten CTS cells are born by using the different fillet radius. The fillet radius is 10 mm to 100 mm, but the cell mass remains constant when the plate has the same thickness. The static deformation, vibration modality and harmonic response of these NPR structures are analyzed in this paper. These CTS cells are modeled by using the finite element method (FEM) with a uniform grids. In static analysis, a surface load and a point load on the top plate are respectively considered to study the elastic deformation, the NPR and the stiffness of CTS cells with different fillet radii and thicknesses. These CTS cells have a greater NPR and a higher stiffness than the original inward hexagonal steel structure. In modal analysis, the natural frequency, the eigenmode and the fixed modality are numerically computed. These frequency values and displacement distributions of CTS cells show that these CTS cells have a higher vibration frequency than the origin inward hexagonal structure cell. In harmonic response analysis, the frequency domain is from 1 Hz to 1000 Hz, and the excitation force is on the top surface of the upper plate. All displacement responses of these CTS cells are analyzed. The harmonic response analysis result shows that the resonance magnitude can be significantly suppressed by these new CTS cells. The analysis result presents the characteristics of this new CTS, and it is beneficial for the vibration isolation in engineering application.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The conventional eigenvalue alarm mode has a high rate of false alarm and missed alarm for the low-speed heavy load super large rolling bearing. Besides, the traditional signal processing method such as envelope spectral analysis is difficult to extract its fault characteristic frequencies, resulting in a high rate of false diagnosis and missed diagnosis. In order to solve the above problems, an intelligent diagnosis method for the low-speed heavy load super large rolling bearing based on deep learning is proposed. The proposed method mainly utilizes the strong robustness of deep learning algorithm to the quality of original vibration data in the field of fault diagnosis. Firstly, an effective signal acquisition scheme is designed to solve the problem that the signal characteristics of low-speed heavy load super large rolling element bearing are difficult to be acquired. Then, the collected data are randomly divided into training sets, verification sets and test sets by using data enhancement technology. Subsequently, input the divided training set samples into 1-dimensional convolution neural network (1DCNN) deep learning model for learning and training to construct the 1DCNN learning model and set network structure parameters. Meanwhile, the optimal training model is obtained by validating the updating effect of model parameters through validation set. Finally, the test data is input into the trained model to realize intelligent diagnosis. Effectiveness of the proposed method is verified by the vibration data of a wind power main bearing.
{"title":"Fault diagnosis of low-speed heavy load super large rolling bearing based on deep learning","authors":"Simin Li, Hongchao Wang","doi":"10.21595/jve.2023.23216","DOIUrl":"https://doi.org/10.21595/jve.2023.23216","url":null,"abstract":"The conventional eigenvalue alarm mode has a high rate of false alarm and missed alarm for the low-speed heavy load super large rolling bearing. Besides, the traditional signal processing method such as envelope spectral analysis is difficult to extract its fault characteristic frequencies, resulting in a high rate of false diagnosis and missed diagnosis. In order to solve the above problems, an intelligent diagnosis method for the low-speed heavy load super large rolling bearing based on deep learning is proposed. The proposed method mainly utilizes the strong robustness of deep learning algorithm to the quality of original vibration data in the field of fault diagnosis. Firstly, an effective signal acquisition scheme is designed to solve the problem that the signal characteristics of low-speed heavy load super large rolling element bearing are difficult to be acquired. Then, the collected data are randomly divided into training sets, verification sets and test sets by using data enhancement technology. Subsequently, input the divided training set samples into 1-dimensional convolution neural network (1DCNN) deep learning model for learning and training to construct the 1DCNN learning model and set network structure parameters. Meanwhile, the optimal training model is obtained by validating the updating effect of model parameters through validation set. Finally, the test data is input into the trained model to realize intelligent diagnosis. Effectiveness of the proposed method is verified by the vibration data of a wind power main bearing.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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