A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. The application of acoustic emission (AE) for bearing diagnosis is gaining ground as a complementary diagnostic tool, however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. Furthermore, the extent of bearing damage has eluded the diagnostician. The experimental investigation reported in this paper was centred on the application of the AE technique for identifying the presence and size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. Comparisons between AE and vibration analysis over a range of speed and load conditions are presented. In addition, the primary source of AE activity from seeded defects is investigated. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis. Furthermore, the AE technique also provided an indication of the defect size, allowing the user to monitor the rate of degradation on the bearing; unachievable with vibration analysis.     

The influence of operating condition on acoustic emission (AE) generation during meshing of helical and spur gear

The application of high-frequency acoustic emission (AE) technology to condition monitoring of gears is still in its infancy. Understanding the influence of gear operating parameters on the generation of AE is essential in applying the AE technology to gear condition monitoring. This paper presents experimental findings on the influence of speed and load in generating AE for operating helical and spur gears. The experimental findings suggest that any percentage reduction in specific film thickness (λ), a direct consequence of a change in load condition, results in a nine- and four-fold percentage change in AE rms for the spur and helical gear sets, respectively. A numerical model representing changes in AE rms with variation in load and speed under near isothermal conditions for spur and helical gears was also established. In conclusion, it is postulated that the AE technology could offer a means of measuring in situ the effectiveness of a lubricant for operational spur and helical gears thereby establishing if the correct lubricating conditions are present to ensure optimal life usage.     

Relationship between calculated film thickness and wear in elastohydrodynamic contacts of gears

Gear oils are important design elements and it is necessary to establish data relating their performance to operating conditions and gear design. Due to a combination of elastic distortion and increased viscosity due to high pressure, a hydrodynamic film is generated in gear tooth contacts. In this paper the authors consider the use of the Dawson and Higginson equation to relate film thickness to the performance of gears with respect to wear rate and present experimental data obtained using the FZG gear test procedure. Initial results of an experimental programme to determine actual pressure viscosity characteristics within highly loaded contacts are also presented, since the authors consider that it is errors in the pressure viscosity relationship that cause the lack of correlation between theoretical film thickness and experimental data on scuffing.     

A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission,vibration and spectrometric oil analysis for spur gears

Prognosis of gear life using the acoustic emission (AE) technique is relatively new in condition monitoring of rotating machinery. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur. Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that based on the analysis of root mean square (rms) levels only the AE technique was more sensitive in detecting and monitoring pitting than either the vibration or spectrometric oil analysis (SOA) techniques. It is concluded that as AE exhibited a direct relationship with pitting progression, it offers the opportunity for prognosis.     

Gear fault identification and localization using analytic wavelet transform of vibration signal

The aim of this present work is to identify and localize the defect in gear and measure the angle between two damaged teeth in the time domain of the vibration signal. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. The enveloping technique is revisited for defect identification but is found unsatisfactory in measuring the angle between two faulty teeth. A signal processing scheme is proposed to filter the noise and to measure the angle between two damaged teeth. The proposed technique consists of undecimated wavelet transform (UWT), which is used to denoise the signal. The analytic wavelet transform (AWT) has been implemented on approximation signal followed by a time marginal integration (TMI) of the AWT scalogram. The TMI graph time-axis is mapped onto the angular displacement of the driver gear. The measurement is shown to identify the first and the second defective teeth impact on gear meshing, which is visible as sharp spikes in the TMI graph. An attempt is also made to replace the approximation from UWT with Intrinsic Mode Function (IMF) derived from the Empirical Mode Decomposition (EMD). The present experimental work establishes the proposed method of measuring and localizing multiple gear teeth defect using vibration signal in the time domain.     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

基于扭振信号的齿轮故障诊断研究

在分析了齿轮啮合扭振的基础上提出了利用轴系扭振信号进行齿轮诊断的方法,度验结果表明扭振信号受环境噪声影响小,对故障敏感,作为齿轮早期故障诊断信息来源的扭振信号优于传统的箱体振动信号,还介绍了一种适用于状态监测的扭振测试方法。     

WN齿轮传动弹流润滑及接触摩擦效率研究

应用WN齿轮接触摩擦与弹流润滑机制结合的方法进行效率分析.依据WN齿轮副啮合原理,创建该齿轮动力润滑与混合摩擦分析模型,分析了啮合中载荷与弹性接触对油膜厚度的影响,探讨油膜形成机制和承载特性;通过啮合过程中的接触摩擦分析,推导出WN齿轮啮合时动力传动效率计算新方程;分析齿轮运转速度、负载及润滑等对传动效率的影响.结果表明:在高速下WN齿轮的传动效率高于渐开线齿轮而在低速下却相反;旋转速度对传动效率的影响要比载荷的影响更大.通过实例计算和试验分析验证了本方法的有效性.     

渐开线直齿轮的摩擦激励及振动阻尼分析

齿轮传动中的润滑油膜一般为非线性粘滞体（Ｒｅｅ－Ｅｙｉｎｇ体）。运用部分膜承载热弹流理论计算齿轮传动中的滑动摩擦力和滚动摩擦力。齿轮的振动阻尼力是齿面滑动摩擦力中的一部分,是齿轮振动角位移的非线性函数。为了便于工程应用,使用线性阻尼系数。它是齿轮几何尺寸和压力油膜的粘度及膜厚的函数。     

EEMD与HMM在齿轮故障诊断方法中的研究

为了解决EMD方法存在的模态混叠的问题,更加精确有效的利用振动信号进行齿轮的故障识别和诊断,提出一种将总体平均经验模态分解(EEMD)和隐马尔科夫模型(HMM)结合的齿轮故障诊断方法。首先对采集到的原始齿轮振动信号进行EEMD处理,获得包含主要故障信息的各阶固有模态函数(IMF)分量,以能量为元素,提取并构造特征向量,对特征向量进行HMM模型训练和诊断测试,来识别齿轮的工作状态和故障类型,实验结果表明,该方法可以有效提高齿轮的故障诊断准确率和精度。     

Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysis

Gears are used for transmission of power, motion or both. Under increased power and higher speeds, tribological failures such as scuffing, pitting, mild wear and tooth breakage are of major concern. This paper presents the results of experimental investigations carried out to assess wear in spur gears of a back-to-back gear box under accelerated test conditions. The studies considered the estimation of specific lubricant film thickness and its effects on the fault growth on gear teeth surface. Ensemble empirical mode decomposition (EEMD) technique is used to extract the fault related features from the vibration signals acquired from the gearbox. The results highlight the advantage of EEMD technique for effective assessment of wear in spurgears.     

Experimental studies on the effects of reduction in gear tooth stiffness and lubricant film thickness in a spur geared system

Gears are one of the most common mechanisms for transmitting power and motion and their usage can be found in numerous applications. Studies on gear teeth contacts have been considered as one of the most complicated applications in tribology. Depending on the application, the speed and load conditions of teeth may change triggering several types of failures on teeth surface such as wear, scuffing, micro-pitting and pitting. The above-mentioned faults influence changes in vibration and acoustic signals, due to changes in operating conditions such as increase in temperature and decrease in lubricant film thickness and specific film thickness. These abnormal changes result in cumulative effects on localised or distributed faults on load bearing surfaces of gears. Such damages cause reduction in tooth stiffness and severity of damage can be assessed by evaluating the same using vibration-based signals.This paper presents the results of experimental investigations carried out to assess wear in spur gears of back-to-back gearbox under accelerated test conditions. The studies considered the estimation of operating conditions such as film thickness and their effects on the fault growth on teeth surface. Modal testing experiments have been carried out on the same gear starting from healthy to worn out conditions to quantify wear damage. The results provide a good understanding of dependent roles of gearbox operating conditions and vibration parameters as measures for effective assessment of wear in spur gears.     

高速大功率密度齿轮传动系统的干摩擦阻尼环减振特性研究

以干摩擦阻尼环对高速大功率密度齿轮传动系统减振特性的影响为研究对象,在考虑干摩擦阻尼环减振作用、轮齿时变啮合刚度、啮合传动误差的基础上,建立干摩擦阻尼环齿轮传动系统的弯扭轴耦合多体动力学模型。设计一套输入最高转速为36 000 r/min、油润滑的齿轮传动系统干摩擦阻尼环减振试验系统,并验证了动力学模型的有效性。研究干摩擦阻尼环结构参数对齿轮传动系统减振特性的影响。结果表明：针对高速大功率密度齿轮传动系统,干摩擦阻尼环具有良好的减振特性,降幅比可以达到20%;通过优化阻尼环结构参数如外圆直径（阻尼环与轮缘间的过盈量）、厚度、宽度等可使齿轮传动系统获得最优减振效果。     

两级三环减速器油膜浮动减振机理的研究

本文介绍了两级三环减速器的结构和工作原理以及油膜浮动均载原理 ,依据振动理论 ,建立了三环减速器环板的振动微分方程 ,导出了工作过程中的位移响应 ,分析了油膜浮动减振机理 ,给出了两级三环减速器油膜浮动的实验结果 ,为改善三环减速器的工作性能提供了有效手段     

Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.     

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.     

振动分析技术在齿轮减速器故障诊断中的应用

利用CSI2130振动分析仪对浆液循环泵齿轮减速器进行振动监测与故障诊断,并使用PeakVue技术进行频谱和时域分析,认为较大的振动是因为齿轮啮合不良而导致输入端齿轮轴断齿造成,停机检修发现输入端齿轮轴有3个齿沿齿轮根部断裂,更换新齿轮轴后,设备运行恢复正常。     

基于振动信号耦合调制新模型和参数辨识的 风电齿轮箱齿圈故障监测

针对风电齿轮箱多级传动导致的振动耦合调制问题,提出了一种考虑级间调幅调频的齿圈故障新模型,并通过参数辨 识技术将其应用于齿圈故障监测。 某级齿圈故障特征频率会以调幅、调频方式,调制不同轮系的啮合频率,呈现出耦合调制现 象,本文针对该特殊调制规律,建立了两级齿圈故障下的振动信号耦合调制模型。 在此基础上,提出基于局部均值分解和列文 伯格-马夸尔特算法的参数辨识技术,确定故障辨识模型的调幅系数,进而可方便地构建出状态指标,以达到齿圈故障监测的目 的。 用现场数据进行验证,结果表明,新模型比传统模型描述振动信号更全面;借助参数辨识技术构建的指标能定位故障齿轮。     

Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size

Gears are one of the most common elements in any rotating machinery. If gear defect can be assessed, gearbox maintenance schedule can be optimally planned. This paper presents an impact velocity model relating measurable vibration signal to the defect size on the gear tooth flank. The analytical model was verified experimentally. The experimental results support the effectiveness of the analytical model in estimating defect size. In addition, experimental vibration signals were decomposed using empirical mode decomposition (EMD) technique. These decomposed oscillatory functions are called intrinsic mode functions (IMFs). Kurtosis value of selected IMF was calculated for early detection of fault.