面向故障诊断的行星齿轮扭振信号测量与分析

行星齿轮箱的运行状态对直升机飞行安全非常重要。行星齿轮箱常工作在恶劣的条件下,容易出现磨损或疲劳裂纹等故障。为实现基于扭振信号的行星齿轮传动故障诊断,采用基于增量式编码器的扭振信号测量方法,分别测量了输入轴和输出轴的扭振信号,并分析对比了信号对齿轮故障的敏感性,结果表明,输入轴信号较输出轴信号能更准确地诊断故障。分析比较了负载对信号的影响,认为在重载情况下输出轴扭振信号比输入轴扭振信号对故障特征更加敏感。     

行星耦合PHEV模式切换过程全频段瞬态扭振特性分析与主动抑制

混合动力车辆模式切换过程中存在多源宽频段激励耦合,进而引发较大瞬态扭振,严重影响驾驶品质。以行星耦合PHEV为研究对象,建立考虑非线性啮合刚度、齿侧间隙以及离合器滑摩的混动系统瞬态扭振模型,选取混合动力车辆行车中启动发动机的典型工况,基于连续小波变换理论,展开各激励因素在全频段下对系统扭振影响特性分析,进一步设计考虑齿轮扭振特性的混杂模型预测控制器,进行宽频段瞬态扭振主动抑制。结果表明,齿轮间隙造成的脱齿-碰撞现象,加剧车辆模式切换过程中10～100 Hz低频扭振(整车层面纵向冲击),同时引发系统10～100 kHz高频扭振(耦合机构层面转矩振荡),而齿轮非线性刚度波动则主要集中在对系统高频扭振的影响。据此建立的考虑齿轮扭振特性的混杂模型预测控制器,将整车冲击度峰值降低47.8%、切换过程高频扭振方均根值降低33.2%,有效提升了驾驶舒适性和混合动力耦合装置使用寿命。     

从扭振评定电梯曳引机的质量

电梯曳引机及其组成部件的设计、制造与装配质量均可利用测得的扭振信号进行分析与判定。文章对扭振信号的形成过程、评价参数、测量装置分别作了论述;对扭振与噪声的关系、扭振与负载的关系以及扭振与故障诊断的示例作了详细的分析,表明通过扭振测定可以判断曳引机的质量。     

行星齿轮箱的定子电流信号诊断方法试验研究

为了解决风力双馈感应发电机（doubly fed induction generator,简称DFIG）行星齿轮箱故障诊断中常规振动检测技术成本高、现场安装与实施困难等问题,提出了一种直接利用DFIG定子电流信号的间接诊断方法。利用电动机、行星齿轮箱和变频器等通用设备,建立了一个简便易行的DFIG传动系统故障模拟试验台。通过模拟试验和Hilbert解调谱分析方法,以行星齿轮为例,研究传动系统的DFIG定子电流信号故障感应机理,探讨故障特征频率成分随发电机运行工况的变化规律。通过与振动加速度信号和扭振信号的对比分析,表明DFIG定子电流信号可以兼顾反映行星齿轮的径向振动与扭振信号的振动特征,可作为实际风电机组常规振动检测技术的一种替代方法。     

渔船轴系扭振信号特征分析

基于小波变换对渔船轴系扭振信号进行了分析。在ANSYS workbench中,建立渔船轴系仿真模型,并导出扭振数据,然后,用自相关函数定性地分析了扭振信号的基本特征,并基于小波变换对渔船轴系扭振信号进行了辨识。通过数据分析验证,所采用的方法可行,具有一定的实用价值。     

基于AR模型和谐波小波的扭振固有频率提取

提出用谱估计的方法分析利用激光多普勒扭振测量仪获取的扭振信号。首次采用 AR模型和谐波小波分解相结合处理低信噪比的扭振信号。首先介绍了 AR模型对信号消噪、重构和谐波小波分解的原理 ,然后利用一组模拟信号来验证该方法的实用性。最后用来处理从实验台上获得的扭振信号 ,提取出了该转子实验台的扭振固有频率 ,与利用传递矩阵法计算的理论结果比较吻合。     

基于最小熵反褶积的行星齿轮箱故障诊断

行星齿轮箱具有传动比大、传动效率高等优点,但比定轴齿轮有更复杂的结构,因常工作在恶劣的条件下,容易出现磨损或疲劳裂纹等故障。扭振信号因信噪比高、频谱结构简单等优点有利于对行星齿轮箱的故障诊断。所以针对重载、强噪声环境下行星齿轮箱故障特征的提取,提出了基于最小熵反褶积(Minimum entropy deconvolution,MED)的扭振信号时域分析方法,该方法通过反转滤波加强冲击特性从而提取行星轮的故障特征。通过对仿真信号和行星齿轮箱的扭振信号分析,在时域上提取了明显的故障冲击特征,并且较频谱分析能更直观清晰地看出故障特性。通过对不同负载情况下的比较,发现该方法对处于大负载情况下的故障诊断效果更佳。     

基于虚拟仪器的转子扭振特性测试系统研究

基于虚拟仪器技术应用于扭振测试领域,开发出一套转子扭振测试系统.扭振试验合同时具备转动轴和摆动轴,可用于对这两种轴的扭振问题进行研究,并可实现多种方式对扭振信号进行测试.数据采集硬件选用了高性能数据采集产品,扭振测试软件基于图形化编程语言LabVIEW开发,能够对扭振信号进行采集,并能快速高效地分析处理扭振数据,显示扭振时域与频域的波形曲线等功能.试验结果表明研制的测试系统能较精确地进行扭振测试,同时便于调整转速,布置测点位置,易于操作、安全可靠.     

轧机扭振非平稳瞬态冲击信号的处理方法

为解决轧机扭振非平稳瞬态冲击信号的故障特征量提取的难题,通过简化轧机动力学模型,构造出一种可以表达轧机扭振非平稳瞬态冲击信号的基元函数,并经哈尔变换和Hilbert-Huang变换的演化,提取出这类扭振信号故障特征.这为解决大型旋转机械设备主传动系统的扭振分析提供了新思路.     

轧机扭振非平稳瞬态冲击信号的处理方法

为解决轧机扭振非平稳瞬态冲击信号的故障特征量提取的难题,通过简化轧机动力学模型,构造出一种可以表达轧机扭振非平稳瞬态冲击信号的基元函数,并经哈尔变换和Hilbert-Huang变换的演化,提取出这类扭振信号故障特征.这为解决大型旋转机械设备主传动系统的扭振分析提供了新思路.     

Monitoring gear vibrations through motor current signature analysis and wavelet transform

In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further, at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. An efficient and new but non-intrusive method to detect the fluctuation in gear load may be the motor current signature analysis (MCSA). In this paper, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA. It has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected. Hence, discrete wavelet transform (DWT) is suggested to decompose the current signal, and FFT analysis is carried out with the decomposed current signal to trace the sidebands of the high frequencies of vibration. The advantage of DWT technique to study the transients in MCSA has also been cited. The inability of CWT in detecting either defects or load fluctuation has been shown. The results indicate that MCSA along with DWT can be a good replacement for conventional vibration monitoring.     

分形在齿轮磨损监测中的应用

将分形理论用于齿轮磨损的监测,从工程应用角度介绍了振动信号盒维数的计算方法。通过对齿轮振动信号分形维数的计算,揭示了分形维数与信号复杂程度之间的内在联系。结果表明:随齿轮磨损的增加,齿轮振动信号盒维数呈下降趋势,运用振动信号的分形维数特征可有效实现齿轮磨损监测。     

Quantitative damage detection for planetary gear sets based on physical models

Planetary gear set is the critical component in helicopter transmission train,and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection.In order to resolve this problem,an approach based on physical models is presented to detect damage quantitatively in planetary gear set.A particular emphasis is put on a feature generation and selection method,which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system.In this feature generation procedure,the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case.Then,a feature based on the spectrum of simulation signals of the dynamical models is generated.Aiming at selecting the best feature suitable for quantitative damage detection,a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing.A feature named SR,which had better performance in tracking damage,is proposed to detect damage in planetary gear set.Meanwhile,the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above,and then the test vibration signal is picked up and used for damage detection.With the results of several experiments for quantitative damage detection,the feasibility and the effect of this approach are verified.The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.     

GEARBOX FAULTDIAGNOSIS BASED ON EMPIRICAL MODE DECOMPOSITION

Time synchronous averaging of vibration data is a fundament technique for gearbox diagnosis. Currently, this technique relies on hardware tachometer to give phase synchronous information. Empirical mode decomposition (HMD) is introduced to replace time synchronous averaging of gearbox vibration signal. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The key problem is how to assure that vibration signals deduced by gear defects could be sifted out by HMD. The characteristic vibration signals of gear defects are proved IMFs, which makes it possible to utilize EMD for the diagnosis of gearbox faults. The method is validated by data from recordings of the vibration of a single-stage spiral bevel gearbox with fatigue pitting. The results show EMD is powerful to extract characteristic information from noisy vibration signals.     

Application of the L-Wigner distribution to the diagnosis of local defects of gear tooth

The detection of impulsive signals causing the fracture of gears is a significant task for the analysis of the characteristics of a damaged gear. However, this impulsive signal is hidden by background noise such as meshing frequencies and broadband noise. Recently, conventional time frequency methods have been used. In the case of a signal with a low SNR, these methods are not sufficient for the detection of impulsive signals; hence, the L-Wigner distribution with an S-method kernel is used and applied to the diagnosis of local defects of a tooth in a gear.     

Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis

Gear is a vital transmission element, finding numerous applications in small, medium and large machinery. Excessive loads, speeds and improper operating conditions may cause defects on their bearing surfaces, thereby triggering abnormal vibrations in whole machine structures. This paper describes the implementation of empirical mode decomposition (EMD) method for monitoring simulated faults using vibration and acoustic signals in a two stage helical gearbox. By using EMD method, a complicated signal can be decomposed into a number of intrinsic mode functions (IMF) based on the local characteristic time scale of the signal. Vibration and acoustic signals are decomposed to extract higher order statistical parameters. Results demonstrate the effectiveness of EMD based statistical parameters to diagnose severity of local faults on helical gear tooth. Kurtosis values from EMD and that obtained from vibration and acoustic signals are compared to demonstrate the superiority of EMD based technique.     

A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis.     

齿轮振动信号分解及其在故障诊断中的应用

对齿轮振动信号的测试及分解进行了研究。根据信号基频,把齿轮振动信号分解为啮合振动与旋转振动,这些振动信号可用于对齿轮状态进行定量研究。基于不同形式的齿轮振动信号,介绍了几种方法来提取信号中的故障信息。利用时域平均技术及齿轮振动信号分解理论对某齿轮箱早期故障信号进行了检测。研究表明,齿轮运动信号分解能够有效检测齿轮的各类故障,高阶加速度信号对齿轮某些类型的早期故障更加敏感。     

Gearbox Deterioration Detection under Steady State, Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smirnov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronons average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.