Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.     

VIBRATION SUPPRESSION OF A FLEXIBLE PIEZOELECTRIC BEAM USING BP NEURAL NETWORK CONTROLLER

This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis (FEA) method is adopted to derive the dynamics model of the system. A back propagation neural network (BPNN) based proportional-derivative (PD) algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.     

Vibration response of rotating mechanical systems using experimental techniques and artificial neural networks

A neural network predictor investigation is presented for analyzing vibration parameters of a rotating system. The vibration parameters of the system, such as amplitude, velocity, and accelertion in the vertical direction, were measured at the bearing points. The system's vibration and noise were analyzed for different working conditions. The designed neural predictor has three layers, which are input, hidden, and output layers. In the hidden layer, 10 neurons were used for this approximation. The results show that the network can be used as an analyzer of such systems in experimental applications.     

Planetary gearbox with localised bearings and gears faults: simulation and time/frequency analysis

Planets bearings of planetary gear sets exhibit high rate of failure; detection of these faults which may result in catastrophic breakdowns have always been challenging. The objective of this paper is to investigate the planetary gears vibration properties in healthy and faulty conditions. To seek this goal a previously proposed lumped parameter model (LPM) of planetary gear trains is integrated with a more comprehensive bearing model. This modified LPM includes time varying gear mesh and bearing stiffness and also nonlinear bearing stiffness due to the assumption of Hertzian contact between the rollers/balls and races. The proposed model is completely general and accepts any inner/outer race bearing defect location and profile in addition to its original capacity of modelling cracks and spalls of gears; therefore, various combinations of gears and bearing defects are also applicable. The model is exploited to attain the dynamic response of the system in order to identify and analyze localized faults signatures for inner and outer races as well as rolling elements of planets bearings. Moreover, bearing defect frequencies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, frequency response of the system for different sizes of planets bearing faults are compared and statistical diagnostic algorithms are tested to investigate faults presence and growth.     

Using a Lumped Parameter Dynamic Model of a Rock Bolt to Produce Training Data for a Neural Network for Diagnosis of Real Data

Ground anchorage systems are used extensively throughout the world as supporting devices for civil engineering structures such as bridges and tunnels. The condition monitoring of ground anchorages is a new area of research, with the long term objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and anchorage post-tension levels. The ground anchorage integrity testing (GRANIT) system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration signals that arise from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. Novel artificial intelligence techniques are used in order to learn the complicated relationship that exists between an anchorage and its response to an impulse. The system has a worldwide patent and is currently licensed commercially.A lumped parameter dynamic model has been developed which is capable of describing the general frequency relationship with increasing post-tension level as exhibited by the signals captured from real anchorages. The normal procedure with the system is to train a neural network on data that has been taken from an anchorage over a range of post-tension levels. Further data is needed in order to test the neural network. This process can be time consuming, and the lumped parameter dynamic model has the potential of producing data that could be used for training purposes, thereby reducing the amount of time needed on site, and reducing the overall cost of the system's operation.This paper presents data that has been produced by the lumped parameter dynamic model and compares it with data from a real anchorage. Noise is added to the results produced by the lumped parameter dynamic model in order to match more closely the experimental data. A neural network is trained on the data produced by the model, and the results of diagnosis of real data are presented. Problems are encountered with the diagnosis of the neural network with experimental data, and a new method for the training of the neural network is explored. The improved results of the neural network trained on data produced by the lumped parameter dynamic model to experimental data are shown. It is shown how the results from the lumped parameter dynamic model correspond well to the experimental results.     

基于遗传神经网络的锚杆极限承载力预测的研究

针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的锚杆极限承载力预测的遗传神经网络模型。该模型以低应变动测的5个变量作为输入变量来对锚杆极限承载力进行预测,并与BP神经网络预测结果进行比较。数值算例表明,遗传神经网络在锚杆极限承载力预测中具有较高的计算效率和识别精度。     

基于声发射监测的静载荷滑动轴承接触摩擦故障诊断的实验研究

在利用振动信号诊断静载荷滑动轴承接触摩擦故障的基础上，利用声发射监测方法对静载荷滑动轴承接触摩擦故障进行诊断．研究表明，与振动信号相比，声发射信号的频率响应范围更宽，所包含的信息量更大，能较好地反映轴承的摩擦规律，故障特征明显，易于识别，参数稳定性较好，因而可以更有效地诊断静载荷滑动轴承接触摩擦故障．     

Investigation on effect of joint clearance on dynamics of four-bar mechanism

As a result of design, manufacturing and assembly processes or a wear effect, clearances are inevitable at the joints of mechanisms. In this study, dynamic response of mechanism having revolute joints with clearance is investigated. A four-bar mechanism having two joints with clearance is considered as a model mechanism. A neural network was used to model several characteristics of joint clearance. Kinematic and dynamic analyses were achieved using continuous contact mode between journal and bearing. A genetic algorithm was also used to determine the appropriate values of design variables for reducing the additional vibration effect due primarily to the joint clearance. The results show that the optimal adjusting of suitable design variables gives a certain decrease in shaking forces and their moments on the mechanism frame.     

基于振动信号神经网络层合板分层损伤检测研究

基于振动信号应用神经网络研究层合板分层损伤的检测方法. 对层合板分层损伤区域, 采 用相同坐标不同节点建立了分层损伤处的有限元模型; 通过数值模拟提取结构无损和 不同程度面积分层损伤的全局振动标识量; 重点研究神经网络对层合板分层损伤位置 和损伤程度的检测技术. 研究表明, 用结构全局振动标识量作为人工神经网络的输入, 对 层合板结构分层损伤检测是一种很有效的工程实用技术, 可应用于实际结构的在线损伤检测.     

基于神经网络算法的建筑结构振动分散控制研究

针对地震作用下建筑结构振动分散控制问题,引入神经网络算法,研究结构振动分散神经网络控制策略,来解决分散控制中各子系统的耦合问题和神经网络算法的训练成本问题.利用径向基函数RBF(Radical Basis Function)神经网络模型并基于newrb函数构建了RBF神经网络控制器,对某20层Benchmark结构模型分别进行集中控制和多工况子系统划分分散控制的数值模拟分析,结果表明,提出的各子系统耦合的分散RBF神经网络振动控制策略考虑了子系统间的信息共享,可有效控制结构的振动响应,且子系统达到理想训练结果所需的训练次数与BP网络相比显著降低.     

中深孔爆破振动参数的BP神经网络预报

以某工程不同爆点不同监测点的爆破振动监测数据为背景 ,在分析爆破振动主要影响因素的基础上 ,建立了能同时对爆破振动速度峰值、振动主频率和振动的持续时间进行预报的BP神经网络模型。该模型的预报结果 (爆破振动的幅值、振动主频率和振动持续时间 )与实际监测结果基本吻合 ,从而得到了该场地不同地质、地形情况下爆破振动预报的BP神经网络模型。     

基于并行卷积神经网络的地磁方向适配性分析

针对地磁方向适配性分析时人工特征提取主观性较强、所取特征难以表达深层的结构性特征的问题,并为了进一步提高方向适配性分析的准确率,提出了一种基于并行卷积神经网络的地磁方向适配性分析方法。首先,从不同角度建立了地磁场在6个代表方向上的适配性分析图;然后,从同一磁场的不同角度出发,利用卷积神经网络自动完成了特征学习,得到了更为全面的方向适配性特征描述;最后,在并行卷积神经网络所得特征的基础上,利用BP网络建立了地磁方向适配性的分析模型。仿真结果证明,该方法可以有效避免人工特征提取和计算等复杂步骤,实现了地磁方向适配性分析的自动化,而且可以获得优于传统网络和单路卷积神经网络的准确率。     

Vibration characteristics of a hydraulic generator unit rotor system with parallel misalignment and rub-impact

The object of this research aims at the hydraulic generator unit rotor system. According to fault problems of the generator rotor local rubbing caused by the parallel misalignment and mass eccentricity, a dynamic model for the rotor system coupled with misalignment and rub-impact is established. The dynamic behaviors of this system are investigated using numerical integral method, as the parallel misalignment, mass eccentricity and bearing stiffness vary. The nonlinear dynamic responses of the generator rotor and turbine rotor with coupling faults are analyzed by means of bifurcation diagrams, Poincaré maps, axis orbits, time histories and amplitude spectrum diagrams. Various nonlinear phenomena in the system, such as periodic, three-periodic and quasi-periodic motions, are studied with the change of the parallel misalignment. The results reveal that vibration characteristics of the rotor system with coupling faults are extremely complex and there are some low frequencies with large amplitude in the 0.3–0.4× components. As the increase in mass eccentricity, the interval of nonperiodic motions will be continuously moved forward. It suggests that the reduction in mass eccentricity or increase in bearing stiffness could preclude nonlinear vibration. These might provide some important theory references for safety operating and exact identification of the faults in rotating machinery.     

涡激响应尾流双振子模型的参数反演方法

尾流双振子模型是研究圆柱结构涡激振动响应的重要模型,模型参数的准确确定对悬浮隧道设计理论具有重要意义.首先通过降阶法将多变量二阶非线性常微分方程组的尾流双振子模型变换为一阶方程组.然后给出一种新型的圆柱结构水槽试验设计方案,其中试验模型的刚度能够较好反映悬浮隧道等实际工程结构的刚度,基于相机动态捕捉和视频识别计算机程序...     

滑动轴承-转子系统非线性裂纹故障特征及其实验研究

由于材料缺陷以及疲劳运转等多种因素，轴承-转子系统常出现轴上裂纹故障，进一步发展会产生断轴巨大事故。本文考虑滑动轴承支承的单盘转子系统，分析轴上裂纹对系统振动特性的影响，并采用谐波小波变换技术分析实验结果，结果表明在充分考虑支承条件的基础上，裂纹的存在不仅在频域上出现了亚频、超频的频率分量，在时域上也有明显的奇异现象。说明谐波小波变换作为时域分析的有力工具，可以用于滑动轴承-转子系统裂纹故障的早期诊断。     

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented.Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory.Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights,with fuzzy output parameters being optimized by genetic algorithm.Such fuzzy neural network was called KBFNN.This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.     

Effect of spalling or tooth breakage on gearmesh stiffness and dynamic response of a one-stage spur gear transmission

Tooth faults affecting gear transmission are always accompanied by a stiffness reduction. In this article an analytical method is proposed to quantify the reduction of gearmesh stiffness due to two common tooth faults: spalling and breakage. Bending, fillet-foundation and contact deflection are taken into account. The dynamic response of a single stage spur gear transmission is computed by using analytical gearmesh issued from analytical modelling and the vibration signatures of each tooth fault is identified.     

航空发动机整机耦合动力学模型及振动分析

面向航空发动机整机振动, 建立了航空发动机转子-滚动轴承-机匣耦合动力学模型. 该模型具有如下特点: (1)考虑转子、滚动轴承及机匣之间的耦合作用; (2)考虑了实际航空发动机的弹性支承及挤压油膜阻尼效应; (3)将转子考虑为等截面自由欧拉梁模型, 运用模态截断法进行分析; (4)考虑了滚动轴承间隙、非线性赫兹接触力以及变柔性VC(Varyingcompliance)振动; (5)考虑了转子与机匣之间的碰摩故障. 运用数值积分方法研究了航空发动机的整机振动规律, 包括: 滚动轴承VC振动分析、弹性支承刚度对耦合系统临界转速的影响、转轴模态截断阶数NM对系统响应的影响分析、挤压油膜阻尼器参数对系统响应的影响分析、突加不平衡的瞬态响应分析以及转静碰摩故障特性分析等.      

Determination of sensor positions for predictive maintenance of revolving machines

The monitoring by measurement and analysis of vibration is largely used to detect the defects in revolving machines. The determination of the best sensor positions is one of the main research goals in the field of predictive maintenance. This paper proposes a numerical methodology based on a finite element model and a spectral analysis in order to find optimum sensor positions. The bearing is a key component for the vibration propagation from the moving parts to static ones. An analytical bearing model and its numerical implementation in a finite element code are presented. The tangent stiffness matrix of the bearing element is obtained by the Newton–Raphson method and then used for the modal and spectral analyses. Several techniques are used to find the most sensitive zones to common defects. The proposed numerical approach correlate well with the experimental results. The numerical modeling of a grinder shows the interests in industrial applications.     

Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults

Rolling element bearings (REBs) play a most critical role in various industrial machinery. A clearly and in-depth understanding of vibration characteristics of REBs is very helpful for condition monitoring and diagnosis applications for the machinery. This work presented a comprehensive review of dynamic modelling and analysis methods for predicting the vibration characteristics of REBs with and without localized and distributed faults. Main capabilities and limitations of those methods for both the localized and distributed faults have been described. Explanations for the generations of the bearing vibrations were also reviewed. A summary of the literature was given followed by the recommendations of current and future research works. Moreover, recent challenges, directions and implications in research works on the dynamic modelling and analysis methods of the localized and distributed faults in REBs have also been conducted.