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.     

基于EMD和非线性峭度的齿轮故障诊断

采用经验模式分解(empirical mode decomposition,简称EMD)和非线性峭度的统计特性对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用EMD分解获得振动响应信号的本征模式函数,用非线性Tea-ger能量算子计算每个本征模式函数的瞬时能量,并对本征模式函数进行系数的非线性峭度计算,提取系统的特征信息。仿真结果表明,用经验模式分解和非线性峭度可实现在线监测齿轮运转工作状态,及时发现齿轮的早期故障,提高了故障检测的可靠性。     

Hilbert-Huang变换在齿轮故障诊断中的应用

为齿轮故障诊断提供了一种新的途径,将Hilbert-Huang变换引入齿轮故障诊断,提出了局部Hilbert能量谱的概念,同时根据齿轮故障振动信号的特点建立了两种基于Hilbert-Huang变换的齿轮故障诊断方法:基于EMD的频率族分离法和Hilbert能量谱方法。采用EMD(Empiricalmodedecomposition)方法对齿轮振动信号能有效地将各个频率族分离;局部Hilbert能量谱可以反映齿轮振动信号的能量随时间和频率的分布情况,从而可以提取齿轮振动信号的故障信息。将这两种方法应用于齿轮故障诊断中,结果表明,基于EMD的频率族分离法和Hilbert能量谱方法都能有效地提取齿轮故障特征信息。     

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Fault diagnosis of gearboxes, especially the gears and bearings, is of great importance to the long-term safe operation. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. The nonstationary vibration signals were analyzed to reveal the operation state of the gearbox. The kernel independent component analysis (KICA) algorithm was used hereby as the BSS approach for the mixed observation signals of the gearbox vibration to discover the characteristic vibration source associated with the gearbox faults. Then the wavelet packet transform (WPT) and empirical mode decomposition (EMD) nonlinear analysis methods were employed to deal with the nonstationary vibrations to extract the original fault feature vector. Moreover, the locally linear embedding (LLE) algorithm was performed as the nonlinear feature reduction technique to attain distinct features from the feature vector. Lastly, the fuzzy k-nearest neighbor (FKNN) was applied to the fault pattern identification of the gearbox. Two case studies were carried out to evaluate the effectiveness of the proposed diagnostic approach. One is for the gear fault diagnosis, and the other is to diagnose the rolling bearing faults of the gearbox. The nonstationary vibration data was acquired from the gear and rolling bearing fault test-beds, respectively. The experimental test results show that sensitive fault features can be extracted after the KICA processing, and the proposed diagnostic system is effective for the multi-fault diagnosis of the gears and rolling bearings. In addition, the proposed method can achieve higher performance than that without KICA processing with respect to the classification rate.     

基于EEMD的行星齿轮箱齿轮裂纹损伤定位

针对行星齿轮式变速箱的齿轮裂纹损伤难以提取特征频率和定位的问题,提出基于总体平均经验模式分解(ensemble empirical mode decomposition,简称EEMD)的齿轮局部损伤频率解调分析方法。该方法在建立的齿轮局部损伤振动信号模型的基础上,分别对太阳轮、齿圈、行星轮的裂纹损伤信号进行EEMD分解和频率解调分析,通过频谱图提取齿轮的局部损伤特征频率,从而识别变速箱中裂纹损伤齿轮的位置。综合仿真分析和试验结果表明,基于EEMD的齿轮局部损伤频率解调分析方法可以有效地提取太阳轮、齿圈和行星轮的裂纹损伤特征频率,实现行星齿轮式变速箱中齿轮裂纹损伤的定位。     

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.     

基于阶次跟踪和经验模态分解的滚动轴承包络解调分析

针对齿轮箱升降速过程中振动信号非平稳的特点,将计算阶次跟踪方法与经验模态分解技术相结合,提出一种研究旋转机械瞬态信号故障诊断的分析方法。首先对齿轮箱启动时测得的振动信号进行时域采样,再对时域信号进行等角度重采样,将其转化为角域准平稳信号,然后对角域里的信号进行经验模态分解得到多个固有模态函数分量,最后对包含轴承故障信息的高频固有模态分量进行包络解调分析。结果显示：阶次跟踪技术能够有效地避免传统频谱方法所无法解决的“频率模糊”现象,将非平稳信号转化为准平稳信号;经验模态分解方法能够提取包含故障信息的固有模态分量,将两种方法相结合是对传统频谱分析法的有力补充,具有很广阔的应用前景。     

齿轮点蚀的多通道数据融合识别方法

针对齿轮箱振动信号中混杂其他零部件振动频率的问题,提出一种基于小波包分解独立分量分析（wavelet package independent component analysis,简称WPICA）和多维经验模式分解（multivariate empirical mode decomposition,简称MEMD）的齿轮箱齿面点蚀故障信号的多通道数据融合识别方法。首先,利用一种窄带独立分量分析(sub-band decomposition independent component analysis,简称SDICA)方法—WPICA,从水泵机组多通道信号中提取齿轮箱振源,确定齿轮箱振动包含的特征频率成分;其次,借助MEMD分解多通道机组振动信号,将所获得的多维固有模式函数(intrinsic mode function,简称IMF)进行矩阵互信息运算,完成多通道数据的融合;最后,通过定义IMF故障敏感因子,确定故障敏感IMF的阶数并获得了齿轮点蚀故障的特征频率。数据分析结果证明了本研究方法的有效性。     

基于包络加窗同步平均的行星齿轮箱特征提取

行星齿轮箱由于行星轮通过效应、太阳轮与行星架的旋转及时变工况,导致其振动响应存在时变传递路径及非平稳性等特点,且传统的同步平均将不能直接应用于行星齿轮箱。笔者在国外加窗同步平均的基础上提出一种能有效克服时变传递路径及非平稳性的基于包络信号角域加窗同步平均的行星齿轮箱故障特征提取方法。首先,基于谱峭度提取出行星齿轮箱振动信号的包络信号;其次,再利用计算阶比跟踪技术对包络信号进行等角度重采样,行星架每旋转一圈,选择合适的窗函数对角域信号进行多齿宽加窗截取;最后,验证齿轮啮合齿序特征,根据重排齿序对加窗信号进行重构振动分离信号,对振动分离信号进行角域同步平均,提取行星齿轮箱故障特征。行星齿轮箱故障实测信号分析表明,该方法能有效提取行星齿轮箱故障特征。     

基于Wigner分布的齿轮箱振动信号相位估计

时域同步平均是齿轮箱诊断技术的基础,目前这种方法依赖于转速传感器提供相位同步信号。探讨了在没有转速传感器的前提下,由振动信号本身得到相位过零信号的方法。建立了齿轮箱振动信号的一种简化理论模型,通过理论分析得到了一种基于Wigner分布的相位估计方法。将这种方法应用于实际的齿轮箱振动数据,证明这种方法是有效的。     

基于EMD和分形的齿轮箱特征提取技术研究

齿轮箱故障诊断实质上是一个特征提取和状态识别的过程,在旋转机械研究中,其特征频率往往是已知或者可以计算的,而实际测量的机械振动信号往往含有故障特征以外的频率成分,为了避免多余信息的干扰,提出了结合EMD和分形几何算法的齿轮箱有效频率分量的特征提取方法。实验表明,该方法可以有效地计算出不同齿轮故障的特征值,实现了定量表征。     

Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum

The empirical mode decomposition (EMD) and Hilbert spectrum are a new method for adaptive analysis of non-linear and non-stationary signals. This paper applies this method to vibration signal analysis for localised gearbox fault diagnosis. We first study the properties of the recently developed B-spline EMD as a filter bank, which is helpful in understanding the mechanisms behind EMD. Then we investigate the effectiveness of the original and the B-spline EMD as well as their corresponding Hilbert spectrum in the fault diagnosis. Vibration signals collected from an automobile gearbox with an incipient tooth crack are used in the investigation. The results show that the EMD algorithms and the Hilbert spectrum perform excellently. They are found to be more effective than the often used continuous wavelet transform in detection of the vibration signatures.     

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.     

Wear detection in gear system using Hilbert-Huang transform

Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions (IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear.     

基于EMD与功率谱分析的滚动轴承故障诊断方法研究

针对西部油田大型设备故障信号的非线性、非平稳特征，提出一种基于经验模态分解方法EMD（empirical mode decomposition）和功率谱的分析方法。首先对滚动轴承振动信号进行经验模态分解，然后对分解后包含轴承故障特征信息的固有模态函数分量作功率谱分析，得到各分量的功率谱图，清晰直观显示出故障特征信号的功率谱，从混有背景信号和噪声的振动信号中提取轴承故障信息。由于EMD方法具有自适应特性，适宜于非线性、非平稳信号的分解，该方法应用于滚动轴承的故障振动信号分析中，结果表明，该方法能够突出滚动轴承振动信号的故障特征，从而提高滚动轴承故障诊断的准确性。     

基于多特征融合的行星齿轮箱点蚀故障诊断研究

为了识别行星齿轮箱的齿面点蚀故障,通过刚柔耦合仿真获得健康和 3 种不同点蚀程度行星齿轮箱的箱体振动信号。对获得的 4 种状态的箱体振动信号进行变分模态分解后,计算每个本征模态函数分量的能量值、峭度因子和信息熵,基于能量值、峭度因子和信息熵多特征融合构建高维特征向量,采用支持向量机分类器对 4 种状态的行星齿轮箱进行识别。结果表明,基于变分模态分解的本征模态函数分量的能量值、峭度因子和信息熵构建的 15 维特征向量,采用支持向量机分类器能够准确识别健康和 3 种不同点蚀程度齿轮的类型。     

基于改进全息希尔伯特谱分析的旋转机械故障诊断方法

时频分析方法能够有效同时提取故障设备振动信号的时间和频率信息,但在全面反映非线性振动信号幅值调制与频率调制特征之间的跨尺度耦合关系方面仍存在局限,且容易受到噪声干扰。对此,创新性地将全息希尔伯特谱分析(Holo-Hilbert spectral analysis,HHSA)方法引入到机械故障诊断中。HHSA通过双层经验模态分解(EMD)结构可完整地描述振动信号的内部调制特性,非常适合机械局部故障的检测。同时,为了进一步提升HHSA的诊断精度、抑制EMD模态混叠和噪声干扰,提出一种基于改进再生相移正弦辅助经验模式分解(Improved regenerated phase-shifted sinusoid-assisted EMD,IRPSEMD)的改进HHSA方法(IHHSA)。通过仿真信号验证IHHSA方法用于局部故障检测和诊断的有效性。最后,将IHHSA应用于齿轮裂纹故障和滚动轴承局部故障诊断中,结果表明,提出的IHHSA方法能够更全面地反映和呈现非线性故障振动信号的内部调制关系,且具有更好的故障识别能力。     

基于多元经验模态分解的旋转机械早期故障诊断方法*

针对旋转机械早期微弱故障诊断问题,提出了基于多元经验模态分解的旋转机械早期故障诊断新方法。首先将多个加速度传感器合理布置在轴承座的关键位置,同步采集多通道振动信息;再利用多元经验模态分解同时对多通道振动信号进行自适应分解,得到一系列多元IMF分量;最后,依据峭度准则和相关系数从中选取包含故障主要信息的IMF分量进行信号重构,提取故障特征。多元经验模态分解方法克服了EMD等方法在进行多通道数据融合时缺乏理论依据的局限性。仿真信号和旋转机械故障信号的实验结果表明,该方法明显优于EEMD方法,对齿轮和滚动轴承故障的检测精度更高,可以在强背景噪声情况下更好地提取出故障冲击特征。     

基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法

针对齿轮箱复合故障的故障特征分离,提出了一种基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法。该方法先根据振动信号中各组成成分形态的差异,采用形态分量分析方法构建不同形态的稀疏表示字典进行故障成分分离,将齿轮箱复合故障信号分解为包含齿轮故障信息的谐振分量、包含轴承故障信息的冲击分量和噪声分量,然后分别对谐振分量和冲击分量进行能量算子解调分析,最后根据各解调谱诊断齿轮和轴承故障。算法仿真和应用实例表明该方法能有效地分离齿轮箱复合故障振动信号中齿轮与轴承的故障特征。     

Thermal analysis MLP neural network based fault diagnosis on worm gears

The importance of fault diagnoses, in any kind of machinery, can’t be over stated. Any undetected small fault in machinery will most probably rise with time and will cause machinery to shut down thus resulting in both mechanical and more importantly economical loss for the industry. In recent years, researches have been done for the faults diagnosis through the analysis of their vibration and sound signatures. The extraction of those characteristic signatures is a complicated process because complexities in modern day machineries can results in many vibration and sound generating sources. This paper presents a condition based fault diagnoses technique to detect the condition of gear. An experimental setup, consisting of a worm gear driven by an electric motor, was setup to conduct tests under different working conditions. The vibration and sound signature signals of worm gear were examined for normal and faulty conditions under different speeds and oil levels. The collected data was then used for feature extraction, by using Fast Fourier Transform to filter background noise signals and to collect only the signature of the gearbox vibration and sound signals. An MLP (Multilayer Perceptron) Artificial Neural Network Model has been developed to classify the signature signals. A thermal camera is also used to observe the heating patterns for all those working conditions. With the help of MLP Artificial Neural Network it is possible to predict the speed and oil level of the gearbox and hence a possible fault diagnoses is also feasible.