Eulerian laser Doppler vibrometry: Online blade damage identification on a multi-blade test rotor

Laser Doppler vibrometry enables the telemetry-free measurement of online turbomachinery blade vibration. Specifically, the Eulerian or fixed reference frame implementation of laser vibrometry provides a practical solution to the condition monitoring of rotating blades. The short data samples that are characteristic of this measurement approach do however negate the use of traditional frequency domain signal processing techniques. It is therefore necessary to employ techniques such as time domain analysis and non-harmonic Fourier analysis to obtain useful information from the blade vibration signatures. The latter analysis technique allows the calculation of phase angle trends which can be used as indicators of blade health deterioration, as has been shown in previous work for a single-blade rotor.This article presents the results from tests conducted on a five-blade axial-flow test rotor at different rotor speeds and measurement positions. With the aid of artificial neural networks, it is demonstrated that the parameters obtained from non-harmonic Fourier analysis and time domain signal processing on Eulerian laser Doppler vibrometry signals can successfully be used to identify and quantify blade damage from among healthy blades. It is also shown that the natural frequencies of individual blades can be approximated from the Eulerian signatures recorded during rotor run-up and run-down.     

基于动态树理论的刀具磨损监测技术

提出了基于动态树理论的刀具磨损监测方法,通过相关系数法提取传感器信号与刀具磨损最相关的几组特征,并采用具有局部记忆的B样条模糊神经网络建立刀具磨损量与声发射信号、切削力信号和振动信号特征之间的非线性映射关系,构造了任意加工条件下的刀具磨损监测系统,刀具磨损的识别结果由集成神经网络输出。试验结果表明,基于此方法构建的刀具磨损监测系统具有精度高、可靠度强、增殖性好和在线识别速度快等优点,值得工业推广。     

叶轮机械叶片故障的叶间动态间距诊断法

提出了叶轮机械叶片故障的叶间动态间距诊断法,该方法采用安装在外壳上的脉冲传感器非接触测量旋转叶片经过传感器探头时产生的脉冲信号,通过分析脉冲间(即叶间)动态的相对或绝对间距,可获取叶片振动和叶片故障信息。提出了刚性叶片和柔性叶片的概念,分析了叶片振动和叶片安装间距误差对动态间距的影响。论述了叶间动态间距诊断法的基本原理、试验研究、实施步骤。试验及现场应用表明该诊断方法是正确的、有效的。     

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade.     

Toolpath dependent chatter suppression in multi-axis milling of hollow fan blades with ball-end cutter

Aiming at the issue of toolpath dependent machining vibration in multi-axis milling of hollow fan blades, this paper presents an optimal selection method of cutting parameters based on single-line toolpath to suppress cutting chatter. Firstly, the impact of hollow structure on the blade structural modal was analyzed by using the modal analysis method. And the unstable regions of hollow blade surface have been predicted, which were prone to induce machining deformation and vibration. Secondly, the relationship between the hollow structure and the dynamic characteristics was revealed by analyzing the dynamic responses to the different cutting positions of blade surface. Thirdly, the optimization of cutting parameters based on single-line toolpath was proposed by establishing the 3D stability lobe diagram. Finally, the feasibility and effectiveness about the analysis of dynamic characteristics and the suppression method of cutting chatter were verified by a milling experiment of hollow blade.     

废气涡轮增压器叶片模态特性的探究

针对增压器常发生的由于叶片振动产生的叶片损坏现象,利用三维CAD建模软件对废气涡轮增压器涡轮叶片及压气机叶片进行建模,并采用有限元分析软件对其分别进行振型模态分析,找出发生共振的频率,为叶片的优化设计提供了理论依据。     

Neural network based expert system for induction motor faults detection

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.     

叶轮机械叶片故障的叶间状态参数诊断法

提出了叶轮机械叶片故障的叶间状态参数诊断法，该方法采用安装在叶轮机械外壳上的传感器非接触测量旋转叶片之间的动态物理参数(即叶间状态参数)，通过分析叶间状态参数，可获取叶片振动和叶片故障的信息。实验表明该诊断方法正确、有效。     

叶片后缘加厚对轴流风机性能的影响

为解决NACA65系列翼型叶片后缘轮廓线内切圆半径收敛至0引起的铸造工艺问题,采用三次多项式函数生成叶片后缘厚度函数,精确调整叶片后缘末端厚度.基于Ansys软件对不同叶片后缘厚度的风机进行流体仿真,并分析其气动性能与静力结构特性.结果表明叶片后缘增厚使得叶片附面层分离损失增加,尾迹与叶栅主流区的掺混损失增大.叶片载荷...     

A non-linear integrated aeroelasticity method for the prediction of turbine forced response with friction dampers

The aim of this paper is to describe an integrated aeroelasticity model for turbine blade forced response predictions. Such an approach requires a successful integration of the unsteady aerodynamics with non-linear structural dynamics, the latter arising from the use of root friction dampers to dissipate energy so that the response levels can be kept as low as possible. The inclusion of friction dampers is known to raise the resonant frequencies by up to 20% from the standard assembly frequencies, a shift that is not known prior to the aeroelasticity calculations because of its possible dependence on the unsteady excitation. An iterative procedure was therefore developed in order to determine the resonance shift under the effects of both unsteady dynamic loading and non-linear friction dampers. The iterative procedure uses a viscous, non-linear time-accurate flow representation for evaluating the aerodynamic forcing, a look-up table for determining the aerodynamic boundary conditions at any speed, and a time-domain friction damping module for resonance tracking. The methodology was applied to a high-pressure turbine rotor test case where the resonances of interest were due to first torsion and second flap blade modes under 40 engine-order excitation. The forced response computations were conducted using a multi-bladerow approach in order to avoid errors associated with “linking” single bladerow computations since the spacing between the bladerows was relatively small. Three friction damper elements, representing one actual friction damper, were used for each rotor blade. The number of rotor blades was decreased by 2–90 to obtain a cyclic sector of 4 stator and 9 stator blades. Such a route allowed the analysis to be conducted on a much smaller domain, hence reducing the computational effort significantly. However, the stator blade geometry was skewed in order to adjust the mass flow rate. Frequency shifts of 3.2% and 20.0% were predicted for the 40 engine-order resonances in torsion and bending modes, respectively. The predicted frequency shifts and the dynamic behaviour of the friction dampers were found to be within the measured range. Furthermore, the measured and predicted blade vibration amplitudes showed a good agreement with available experimental data, indicating that the methodology can be applied to typical industrial problems.     

基于虚拟传感器内插法的叶片高倍频振动辨识方法

叶片的振动及其导致的高周疲劳断裂问题是当前高性能透平机械研发与运行中面临的主要问题,高负荷叶片的流动参数在尾迹的干扰下发生周期性脉动造成高倍频振动是造成高周疲劳的主要因素,因此叶片高倍频振动的监测对高周疲劳的诊断预警具有重要意义。提出了基于虚拟传感器内插法的旋转态叶片高倍频信号重构及辨识方法,可实现高倍频的求解。在传感器安装夹角为6°的情况下可实现高达60倍频以内的振动辨识,理论上只需2支传感器即可进行倍频的识别,实际应用中采用4支传感器即可保证辨识精度,且只需一次启车或停车就可实现叶片振动频率的识别,简化了测试步骤。所提出的辨识方法可应用于航空发动机等静叶片数量较多、振动形式为高阶次高倍频的高端旋转机械的振动监测识别,模拟仿真和试验结果验证了该方法的有效性及准确性。     

An optimal design for axial-flow fan blade: theoretical and experimental studies

The technique of inverse design problem (IDP) for optimizing the three-dimensional shape of an axial-flow fan blade based on the desired airflow rate is presented in this work. The desired volume flow rate of air can be obtained from the airflow rate of the existing axialflow fan by multiplying it with a constant which is greater than unity. The geometry of the redesigned fan blade is generated using numerous design variables, which enables the shape of the fan blade to be constructed completely; thus the technique of parameter estimation for the inverse design problem can be used in this study. Results show that with the redesigned optimal fan blade, the airflow rate of fan can be increased, thereby improving the performance of the axial-flow fan. Finally, to verify the validity of this work, the prototypes of the original and optimal axial-flow fan blades are fabricated and fan performance tests are conducted with these blades on the basis of the AMCA-210-99 standard. The algorithm used in the present study can be applied to the blade design problem in any propulsion and power systems.     

基于神经网络的智能叶片优化设计系统的研究

为给叶轮机械叶片设计人员提供一个操作灵活、专业性强、功能齐全的叶片设计软件，应用神经网络技术开发了一套具有智能化功能的叶片优化设计系统。利用某三级轴流压气机静子叶片2以及某两级风扇压气机转子叶片2的叶片优化改型设计对软件进行了验证，优化设计前后两个叶片流场气动性能的对比分析结果表明，该软件界面友好，功能齐全，具有一定的智能功能，能够满足叶片设计以及优化设计的基本要求。     

基于神经网络的智能化机构振动主动控制实验研究

进行了基于神经网络的智能化机构振动主动的实验研究，设计了具有压电陶瓷作动器与电阻应变计传感器的宏观智能化机构实验装置及其振动控制系统；根据实验样本数据，离线设计了神经网络控制器；采用基于神经网络的直接自校正控制策略对智能化机构实施了在线控制，机构的动力学品质得到了显著改善。     

带缘板摩擦阻尼器叶片的非线性振动响应研究

发展了一种用于计算带摩擦阻尼器叶片非线性响应的时频转换方法。该方法能够方便地计算接触面非线性摩擦力，考虑响应的高次谐波以及叶片高阶振型的影响。对带缘板阻尼器的双平板叶片进行了振动试验并对试验模型进行了理论计算，讨论了各模型参数对缘板阻尼器减振效果的影响。通过试验结果与理论结果的比较，验证了理论方法的正确性。该方法稍加推广即可研究循环叶片组的振动特性，为指导带缘板阻尼器叶片的减振设计奠定了基础。     

基于叶尖定时的非接触式旋转叶片异步振动分析

基于叶尖定时原理设计一套旋转叶片振动实时检测系统,该系统主要由光纤传感、数据采集、振动分析三个部分组成。光纤传感的核心是叶尖定时传感器和光电转换模块的设计,数据采集实现对转换后的脉冲数据进行高速采集、预处理和与计算机之间的高速通信,振动分析则由计算机软件进行实时处理并显示分析结果。整套系统在现场某大型压气机上进行了原理性验证,同时在高速模拟转子上通过了实时性验证,异步振动分析结果与应变片监测的数据完全吻合,保证了整套系统最终应用于压气机叶片振动的在线实时监测。     

Fault detection of reciprocating compressor for small-type refrigerators using ART-Kohonen networks and wavelet analysis

This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines.     

基于频响函数的结构损伤检测

对一拉索结构的正常状态及两种损伤状态进行了模态测试 ,用频响函数的曲率来进行损伤检测。结果表明 :频响函数的曲率对损伤非常敏感 ,不仅反映出了损伤位置 ,而且能够表示损伤程度。     

不同展弦比下扭转叶片振动特性分析

以燃气轮机扭转叶片为研究对象,建立不同展弦比下的叶片结构模型,对叶片的固有频率求解,对叶片不同展弦比下振动特性进行讨论分析,得出各展弦比下叶片的固有振动特性规律。结果表明:定宽度时,叶片的高阶次和小展弦比区域振动频率受展弦比影响更为敏感,且随着展弦比增加,叶片的各阶固有频率均降低;定长度时,叶片的弯曲振动频率,随着展弦比的增加而增大,而扭转振动频率却出现一定幅度的上升,同时叶片的扭转振动频率较弯曲振动频率变化更明显。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。