滚动轴承的刚度分析方法与试验测定

本文基于Hertz接触理论建立轴承零件模型,在分析轴承零件受力的基础上,给出了轴承内部零件相互作用的具体表达式,分析了轴承载荷(轴向、径向、力矩载荷)对轴承接触应力和接触变形的影响,获得了滚动轴承整体五自由度刚度矩阵的表达形式.在滚动轴承加载试验台上,通过测量不同载荷作用下轴承的轴向位移与径向位移,获得了轴承的刚度特性,与轴承刚度理论计算结果对比,验证模型正确性.     

转子-轴承系统中滚动球轴承的非线性动力学相似特性研究

针对转子—轴承系统中滚动球轴承的动力学相似问题,提出一种考虑非线性振动特性的轴承系统相似模型建立方法.首先,建立滚动球轴承整体的非线性振动微分方程,运用积分模拟法推导了轴承整体的非线性振动特性相似关系,并结合滚动球轴承的动力学相似关系得到滚动球轴承系统的相似设计准则;其次,应用所得的相似准则,以深沟球轴承C204JUT为原型、6208为模型进行数值仿真实例计算,通过采用Newmark-β算法计算得到的分叉图分析了转速ω、径向载荷F_r、阻尼C及径向游隙γ大小对原型和模型轴承系统振动位移或速度响应的影响;最后,通过对比原型和模型的各参数(ω、F_r、C、γ)分叉图中分叉区间、趋于稳定运动参数值大小以及进入稳定周期运动时的稳态响应值大小验证相似准则的准确性和有效性.通过分析得到以下结论:1滚动球轴承非线性振动特性参数(如振动响应、结构阻尼等)相似关系可由轴承结构参数相似关系确定;2原型与模型非线性运动的分叉图形状一致,且模型能够很好的预测原型稳态振动响应,因此可将模型轴承用来预测原型轴承的非线性振动行为.     

Fault diagnosis of rolling element bearings via discriminative subspace learning: Visualization and classification

Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. To address such concern, we extend our algorithm for solving trace ratio problem in linear discriminant analysis to diagnose faulty bearings in this paper. Our algorithm is validated by comparison with other state-of art methods based on a UCI data set, and then be extended to rolling element bearing data. Through the construction of feature data set from sensor-based vibration signals of bearing, the fault diagnosis problem is solved as a pattern classification and recognition way. The two-dimensional visualization and classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis.     

A computer-aided selection procedure for rolling element bearings

A computer-aided selection procedure for rolling element bearings is described and its use illustrated by examples of computer printout. The programs are written in the interactive Focal language for use on a PDP-8 computer. To avoid the need for extensive computer storage of bearing data, certain data for the bearing under test have to be entered during each program run.     

基于BA-WPHM的滚动轴承两阶段剩余寿命预测方法

为了提高滚动轴承剩余寿命预测的准确性,根据滚动轴承运行过程的两阶段性特点,提出了一种基于蝙蝠算法(BA)和威布尔比例风险模型(WPHM)的滚动轴承两阶段剩余寿命预测方法。首先,构建基于WPHM的剩余寿命预测模型;其次,提出了两阶段极大似然估计法,建立新的似然函数,并利用BA算法进行求解,以提高参数估计的准确性;最后,建立BA-WPHM模型对滚动轴承进行剩余寿命预测。案例分析表明,相比于Newton-Raphson算法、自组织分层猴群算法(SHMA)和独特的自适应粒子群算法(UAPSO),提出的方法参数估计的准确性更高,剩余寿命的预测精度优于支持向量回归(SVR)方法,验证了所提方法的有效性,为滚动轴承维修决策的可行性提供了依据。     

基于小波及AR模型的机车滚动轴承故障特征提取

滚动轴承失效是机车牵引传动系统的主要故障源之一。为了有效诊断滚动轴承故障,提出了基于小波变换及AR模型参数的机车滚动轴承特征提取方法,以提取能准确反映滚动轴承运行状态的特征信息。首先,通过小波变换对滚动轴承运行时产生的非平稳振动信号进行分解重构,得到不同尺度下的重构信号;然后对重构信号建立AR模型,提取AR模型的自回归参数作为表征滚动轴承运行状态的特征;最后采用支持向量机分类器对提取的特征进行故障分类与识别。仿真结果表明机车滚动轴承故障得到了有效诊断。     

Kinematics and control of multifingered hands with rolling contact

The kinematics of rolling contact are derived for two surfaces of arbitrary shape rolling on each other. The kinematic equations are applied to a multifingered hand manipulating some object of arbitrary shape in three dimensions, and a scheme is presented for the control of such a hand which is a generalization of the computed torque method of control of robot manipulators. In implementing the control, it is required that all applied forces lie within the friction cone of the object so that sliding does not occur. The theory has been validated by dynamic graphical simulations of the resulting closed-loop system for several examples     

KNN-朴素贝叶斯算法的滚动轴承故障诊断

滚动轴承的故障诊断对于提高工业生产效率,保障工业生产的稳定安全地运行具有重要意义。为了提高滚动轴承故障识别的正确率,提出一种使用KNN-朴素贝叶斯决策组合算法对滚动轴承故障诊断。组合算法利用朴素贝叶斯算法对使用不同K值的KNN算法初步分类结果进行再分类以达到提高滚动轴承故障识别的目的。首先,使用小波包能量法对滚动轴承振动信号进行能量特征提取,然后使用多个参数K值不同的KNN算法对能量特征数据预分类,得到多个KNN算法分类结果集,将分类结果集进行处理得到预分类结果集,将预分类结果集作为朴素贝叶斯算法的输入,使用朴素贝叶斯算法对数据再分类。实验结果表明,组合算法相较于传统KNN算法及贝叶斯算法在滚动轴承的故障诊断率得到了有效提高,实现了对滚动轴承故障的有效诊断。     

An efficient method for inverse dynamics of manipulators based on the virtual work principle

The computational efficiency of inverse dynamics of a manipulator is important to the real-time control of the system. For serial manipulators, the recursive Newton-Euler method has been proven to be the most efficient. However, for more general manipulators, such as serial manipulators with closed kinematic loops or parallel manipulators, it must be modified accordingly and the resultant computational efficiency is degraded. This article presents a computationally efficient scheme based on the virtual work principle for inverse dynamics of general manipulators. The present method uses a forward recursive scheme to compute velocities and accelerations, the Newton-Euler equation to calculate inertia forces/torque, and the virtual work principle to formulate the dynamic equations of motion. This method is equally effective for serial and parallel manipulators. For serial manipulators, its computational efficiency is comparable to the recursive Newton-Euler method. For parallel manipulators or serial manipulators with closed kinematic loops, it is more efficient than the existing methods. As an example, the computations of inverse dynamics (including inverse kinematics) of a general Stewart platform require only 842 multiplications, 511 additions, and 12 square roots.     

A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing

In this study, a novel deep convolutional neural network-bootstrap-based integrated prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is developed. The proposed architecture includes two main parts: 1) a deep convolutional neural network–multilayer perceptron (i.e., DCNN–MLP) dual network is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and to predict the RUL of bearings, and 2) the proposed dual network is embedded into the bootstrap-based implementation framework to quantify the RUL prediction interval. Unlike other deep-learning-based prognostic approaches, the proposed DCNN-bootstrap integrated method has two innovative features: 1) both 1D time series-based and 2D image-based features of bearings, which can multi-dimensionally characterize the degradation of bearings, are comprehensively leveraged by the proposed dual network, and 2) the RUL prediction interval can be effectively quantified without relying on the bearing’s physical or statistical prior information based on bootstrap implementation paradigm. The proposed approach is experimentally validated with two case studies on rolling element bearings, and comparisons with other state-of-the-art techniques are also presented. Subsequently, our code will be open sourced.     

Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform

This paper is focused on comparison of effectiveness of artificial intelligence (AI) techniques in fault diagnosis of rolling element bearings. The features for classification are extracted through wavelet packet decomposition using RBIO 5.5 wavelet. The whole classification is done using two features: energy and Kurtosis. The data samples for classification are taken with reference to a healthy bearing, thus, minimizing the errors from the experimental set-up. Four bearing conditions such as bearing with outer race defect, inner race defect, ball defect and combined defect on outer race, inner race and ball have been used in this paper. Localized defects of micron level are induced through laser machining. The effectiveness of three AI techniques viz. ANN, SVM and multinomial logistic regression are compared. The results show that the Logistic Regression technique is the more effective than other two techniques as ANN and SVM.     

Local rolling and tilting capability analysis of fully parallel linear actuated platform-type manipulators

Two problems associated with the local rotatability of two types of fully parallel linearly actuated platform manipulators are investigated in this paper. The first problem is to find the physically allowable region that the movable platform can be freely rolled about any given direction at any specified position. The second problem is to evaluate the maximum angle that the movable platform can be tilted about any given position and orientation. The kinematic constraints involved in these problems are the stroke limitation of the linear actuators, the motion constraints of the passive spherical and universal joints, and the interference condition between the supporting limbs. A unified and computationally efficient approach for solving these problems which takes into account all of the kinematic constraints is developed.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Dynamic characteristics of a coupled journal and thrust hydrodynamic bearing in a HDD spindle system due to its groove location

 This research numerically analyzes the dynamic characteristics of a coupled journal and thrust hydrodynamic bearing due to its groove location which has the static load due to the weight of a rotor in the axial direction and the dynamic load due to its mass unbalance in the radial direction. The Reynolds equation is transformed to solve a plain member rotating type of journal bearing (PMRJ), a grooved member rotating type of journal bearing (GMRJ), a plain member rotating type of thrust bearing (PMRT), and a grooved member rotating type of thrust bearing (GMRT). FEM is used to solve the Reynolds equations in order to calculate the pressure distribution in a fluid film. Reaction forces and friction torque are obtained by integrating the pressure and shear stress along the fluid film, respectively. Dynamic behaviors, such as whirl radius or axial displacement of a rotor, are determined by solving its nonlinear equations of motion with the Runge–Kutta method. This research shows that the groove location affects the pressure distribution in the fluid film and consequently the dynamic performance of a HDD spindle system. Received: 5 July 2001/Accepted: 17 October 2001     

随机森林在滚动轴承故障诊断中的应用

针对不同轴承数据特征选择困难和单个分类器方法在滚动轴承故障诊断中精度较低的问题,提出了一种基于分类回归树（CART）的随机森林滚动轴承故障诊断算法。随机森林是包含了多种分类器的集成学习方法。通过随机森林的“集成”思想来提高滚动轴承故障诊断的精度。从滚动轴承的振动信号中提取时域统计指标,将其作为特征向量,利用随机森林（Random Forest）对滚动轴承故障进行诊断。利用SQI-MFS实验平台的轴承数据,与传统分类器（SVM、kNN和ANN）以及单个分类回归树的诊断结果相比,随机森林算法具有比较高的诊断精度。     

一种滚动轴承故障诊断方法

针对基于支持向量机的滚动轴承故障诊断方法中支持向量机的参数优化问题,提出一种改进的果蝇优化算法,即以模式分类准确率作为果蝇味道浓度函数,并采用该算法来优化支持向量机模型的惩罚因子和核函数参数;基于改进果蝇优化算法和支持向量机对滚动轴承的故障模式进行分类诊断,结果表明改进的果蝇优化算法具有较高的收敛速度和寻优效率,基于该算法和支持向量机的滚动轴承故障诊断方法具有较高的分类准确率。     

Singularities in motion and displacement functions for a 7 degree-of-freedom manipulator

The authors propose a method for the determination of singularities in motion and displacement functions for a seven degree-of-freedom manipulator. The manipulator is considered, hereby, as a set of six degree-of-freedom manipulators. It is proven that two types of singularities in motion can occur at link positions that are independent and dependent with respect to the trajectory to be executed. The relations between the structure of singularity equations and displacement equations are discussed. The derivation of displacement equations for a manipulator with singularities of the second type is based on the idea of modeling of a manipulator by two open kinematic chains and invariants that may be found for these chains. The inverse kinematic equations and the equations of singularities in motion have been derived using a symbolic computer program which can handle manipulators of general structure (with five, six, and seven degrees-of-freedom). This program is written in the Symbolic Computation Language REDUCE.     

高速轴承动力学有限元分析方法

高速轴承是高速列车安全运行的重要部件,轴承运转过程中的动态特性直接影响 轴承的使用寿命,采用最小部件之间创建面面接触的方式,建立高速轨道客车用双列圆锥滚子 轴承的三维虚拟样机模型,利用ANSYS/LS-DYNA 分别研究客车直线匀速行驶和额定速度下以 最小转弯半径行驶两种工况下轴承运转过程的动态接触特性,得到轴承的速度特性、加速特性 以及滚子承载分布状况、接触单元法向作用力时间历程变化曲线以及保持架的振动曲线,研究 结论可以为高速轴承设计过程中模型构建方案的合理确定提供参考。     

基于深度在线迁移的变负载下滚动轴承故障诊断方法

针对变负载条件下滚动轴承源域与目标域中相同状态的数据特征分布差异性较大,目标域数据按照序列方式在线获取时,数据更新需重新训练模型的问题,提出一种深度在线迁移的CNN-ISVM(convolutional neural networks-incremental support vector machine)变负载下滚动轴...     

Tribological design of a multistep journal bearing

Journal bearings are machine elements with widespread application. The extent of their use, from the automobile engines to gas turbines, gives a significant incentive for improvement of their performance. Various approaches focusing on the journal bearing design have been used in the past in order to reduce power losses and increase load carrying capacity of journal bearings, including artificial texturing, axial and circumferential grooves and bushing specific shapes (e.g. three-lobe bearings). In this paper, the multistep journal bearing is examined. Its performance benefits are quantified. A maximum friction coefficient improvement of 38% is obtained with simultaneous 9.7% improvement of load capacity in comparison to the plain bearing. The specific operating conditions under which these benefits occur are established. The physical mechanism responsible for these improvements is discussed by correlating pressure, shear stress distribution, lubricant thickness and bushing geometry. The ultimate objective of this work is to suggest certain design principles that may adjust the improvement of the desired operating characteristics of the multistep journal bearing. The surface configuration of the bushing is relatively simple and is thus easy to be manufactured.