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Model test and numerical analysis of height restriction frame to over-height vehicle impact 超高车辆撞击限高架的模型试验和数值分析
IF 1 Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-25 DOI: 10.21595/jve.2023.23324
Yan Zhou, Zhushan Guo, Kai Zhang, Jinzhi Yi
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.
本研究开发了一个包含超高车辆和限高架(HRF)的简化模型,以探索限高架在受到超高车辆撞击时的失效模式和机械性能。在研究范围内,建立了用于模拟分析的严格模型试验。通过将测试结果与计算结果进行比较,确认了这些数值模拟的有效性。研究还分析了不同速度和重量的车辆在撞击 HRF 时的动态响应。研究结果表明,横梁的大部分位移可归因于支柱的倾覆,而较小部分则是由于横梁的塑性变形。柱的位移主要是由其自身的倾覆引起的。梁和柱的底座都有弹塑性变形的迹象。据观察,关键节点的位移和应力随着车速和重量的增加而增加。与车重相比,车速是影响车辆冲击力的主要因素。此外,车辆重量的增加延长了车辆与 HRF 之间的碰撞时间,这表明车辆重量在立柱倾覆中起着重要作用。研究结果可为 HRF 的设计和计算提供实验和理论参考。
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引用次数: 0
Influence of a nonlinear asymmetric shock absorber on vibration of a bus subjected to harmonic excitation 非线性非对称减震器对受谐波激励的总线振动的影响
IF 1 Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-25 DOI: 10.21595/jve.2023.23404
Huu Nhan Tran, Fergyanto E Gunawan, Ngoc Dai Pham
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。
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引用次数: 0
Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method 基于自适应反离群无特征卡尔曼滤波器和 GA-BPNN 方法的车辆状态和参数估计
IF 1 Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-18 DOI: 10.21595/jve.2023.23441
Yingjie Liu, Dawei Cui, Wen Peng
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。
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引用次数: 0
A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network 基于卷积神经网络的机械故障诊断方法综述
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-04 DOI: 10.21595/jve.2023.23391
Junjian Hou, Xikang Lu, Yudong Zhong, Wenbin He, Dengfeng Zhao, Fang Zhou
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.
机械故障诊断可以防止机械设备故障的恶化,对机械设备的稳定运行具有重要意义。本文首先综述了故障诊断的三种基本方法和数据驱动故障诊断的常用方法,重点介绍了深度学习和卷积神经网络的特点和优势。然后介绍了CNN(卷积神经网络)的基本结构和工作原理,以及达到较好训练效果的一些基本方法。其次,从数据处理、数据融合、样本集构建等方面综述了基于CNN的故障诊断方法及其应用场景和优缺点;另一方面,介绍了迁移学习的相关知识和概念,综述了迁移学习与卷积神经网络相结合的机械故障诊断技术的一些应用场景和优缺点。最后,讨论了卷积神经网络目前面临的困难和挑战,展望了卷积神经网络应用于故障诊断领域的研究方向。虽然已有不少类似的文献综述,但本文旨在介绍故障诊断的基本方法,提出数据驱动、CNN在故障诊断领域的基本应用,以及迁移学习(Transfer Learning)和CNN在故障诊断中的应用场景和优缺点,以及存在的问题和展望。它有助于研究人员对此有一个基本的了解。
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引用次数: 0
Seismic performance of fabricated shear wall structures with design defects 具有设计缺陷的装配式剪力墙结构的抗震性能
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-04 DOI: 10.21595/jve.2023.23508
Hua Yan, Bo Song, Huanhuan Yin
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.
套筒灌浆连接是装配式施工中预制剪力墙互连的常用方法。在现场施工领域,如何完全避免套筒注浆缺陷仍然是一个挑战。为了研究存在套筒灌浆缺陷的剪力墙结构的抗震性能和随后的损伤进展,利用ABAQUS软件建立了一个两层剪力墙模型。采用低循环往复加载的数值模拟,研究在三种不同的运行环境下进行:无缺陷、局部缺陷和全面缺陷。结果提供了洞察加剧的混凝土损伤所引发的缺陷存在于剪力墙结构。这些缺陷会导致钢筋过早屈服,从而导致钢筋塑性长度的扩大,从而阻碍钢筋与混凝土的协调变形。在初期加载阶段,“夹紧现象”在完全缺陷结构中尤为明显。随着循环荷载的增加,缺陷和无缺陷结构的滞回曲线趋于收敛。无论是否存在注浆缺陷,结构的骨架曲线在反力达到峰值前均表现出显著的均衡性,而存在缺陷的结构在反力达到峰值前表现出过早的衰减。这反过来又降低了结构变形预警的效果,危及安全。综上分析可知,套筒灌浆缺陷的存在对剪力墙结构的抗震性能和后续损伤轨迹有显著影响。因此,在现场施工中解决和减轻这些缺陷是维护结构综合安全和稳定的必要前提。
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引用次数: 0
Dynamic behavior of risers under nonlinear oceanic environmental loading 非线性海洋环境载荷下立管的动力特性
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-11-04 DOI: 10.21595/jve.2023.23497
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.
立管系统是连接海底防喷器和钻井平台的重要环节。立管受顶部张力和海洋环境力等因素的影响,会发生变形和磨损,一旦发生故障,会带来环境污染和经济损失的风险。因此,本研究考察了隔水管对海洋环境载荷的动态响应。首先利用最小势能原理和变分求极值法推导了非线性海洋载荷作用下立管系统的运动微分方程。随后,建立了基于Morrison方程的非线性波流负荷模型,并利用三阶Hermite插值函数和Galerkin加权残差法将该模型离散为有限元模型。最后,采用Newmark数值积分法分析了隔水管的动力响应。该研究还探讨了海洋环境参数和钻井参数对隔水管动态行为的影响。数值结果对比分析表明,立管的最大位移出现在中部,最大挠度出现在立管末端。挠度角响应的周期性受隔水管位置的影响,从中部到末端呈现先减小后增大的趋势。值得注意的是,顶部张力和表面潮流速度对隔水管的动力行为有显著影响。研究结果为隔水管可靠性评估和运行参数的确定提供了理论依据。
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引用次数: 0
Dynamic characteristics analysis for gear transmission system in shearer cutting section under different loads 采煤机截割段齿轮传动系统在不同载荷下的动态特性分析
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-10-31 DOI: 10.21595/jve.2023.23478
Guo Ye, Xing Deng, Jinyong Ju, Lianchao Sheng
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.
齿轮传动系统是采煤机切削部分的重要组成部分。其性能的好坏直接影响到整个系统的可靠、高效运行。分别建立了多刚体和刚柔耦合模型,并在Adams虚拟仿真软件中对采煤机截割段齿轮传动系统进行了动力学分析。研究了不同负载条件下齿轮传动系统的动态特性。探讨了恒负载转矩和阶跃负载转矩对驱动系统动态特性的影响。研究结果表明,基于齿轮传动系统刚柔耦合模型的仿真结果更接近实际工况。它提供了一种可视化的动态分析手段,更加直观和方便。研究方法和结果可为进一步探索电机-齿轮传动系统的机电耦合动态特性提供参考。
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引用次数: 0
Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN) 基于新型无监督深度神经网络(DNN)的刀具状态监测研究
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-10-31 DOI: 10.21595/jve.2023.23361
Jingjing Gao, Jing Liu, Xinli Yu
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.
为了提高刀具磨损监测的识别精度和准确度,提出了一种基于堆栈去噪自编码器(SDA)的无监督深度神经网络(DNN)。经过特征提取和选择,堆栈去噪自动编码网络降低特征向量的维数。在此基础上,利用主成分分析(PCA)和t分布随机邻居嵌入(t-SNE)对特征进行两次降维,最终得到一个简单的二维特征矩阵。最后,通过增加SoftMax回归层建立SDA深度神经网络模型,并将刀具磨损监测结果作为新的标记数据,通过二次反向传播对深度神经网络参数进行微调。实验结果表明,该方法能够自适应学习并获得有效的特征表达,刀具磨损状态识别结果具有较高的准确性。该方法能有效识别刀具的磨损状态。
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引用次数: 0
Mechanical characteristics of a corset type structure with negative Poisson’s ratio 负泊松比紧身胸衣结构的力学特性
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-10-30 DOI: 10.21595/jve.2023.23413
Yuchao Song, Yanxin Yang, Changkuan Chi, Guobin Li, Jiahui Zhang, Zhaowen Zhang
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.
针对机械超材料及其隔振能力,在向内六角形钢结构的基础上,在向内角处加圆角,设计了一种新型的束腰式结构。利用不同的圆角半径生成10个CTS细胞。圆角半径为10mm ~ 100mm,但当平板厚度相同时,细胞质量保持不变。本文分析了这些结构的静变形、振动模态和谐波响应。采用均匀网格的有限元方法对这些CTS单元进行了建模。在静力分析中,分别考虑顶板的表面荷载和点荷载,研究不同圆角半径和圆角厚度的CTS单元的弹性变形、NPR和刚度。这些CTS细胞比原来的内向六角钢结构具有更大的NPR和更高的刚度。在模态分析中,对固有频率、特征模态和固定模态进行了数值计算。CTS细胞的频率值和位移分布表明,这些CTS细胞具有比原始内向六边形结构细胞更高的振动频率。在谐波响应分析中,频域范围为1hz ~ 1000hz,激振力在上板顶面上。对CTS单元的位移响应进行了分析。谐波响应分析结果表明,这些新型CTS细胞可以显著抑制谐振幅度。分析结果揭示了这种新型隔振器的特性,有利于工程应用中的隔振。
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引用次数: 0
Fault diagnosis of low-speed heavy load super large rolling bearing based on deep learning 基于深度学习的低速重载超大型滚动轴承故障诊断
Q4 ENGINEERING, MECHANICAL Pub Date : 2023-10-08 DOI: 10.21595/jve.2023.23216
Simin Li, Hongchao Wang
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.
对于低速重载超大型滚动轴承,传统的特征值报警方式存在较高的虚警和漏警率。此外,包络谱分析等传统的信号处理方法难以提取其故障特征频率,导致其误诊断和漏诊率高。为解决上述问题,提出了一种基于深度学习的低速重载超大型滚动轴承智能诊断方法。该方法主要利用深度学习算法对原始振动数据质量的强鲁棒性进行故障诊断。首先,针对低速重载超大型滚动轴承信号特征难以获取的问题,设计了有效的信号采集方案;然后,利用数据增强技术将采集到的数据随机分为训练集、验证集和测试集。随后,将划分好的训练集样本输入到一维卷积神经网络(1DCNN)深度学习模型中进行学习和训练,构建1DCNN学习模型并设置网络结构参数。同时,通过验证集验证模型参数的更新效果,得到最优训练模型。最后将测试数据输入到训练好的模型中,实现智能诊断。通过某风电主轴承的振动数据验证了该方法的有效性。
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引用次数: 0
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Journal of Vibroengineering
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