含双裂纹齿轮副轮齿裂纹扩展寿命分析

裂纹扩展是齿轮传动的主要故障,而且裂纹所处位置对裂纹扩展行为作用明显。为探讨齿轮副轮齿裂纹位置与裂纹扩展寿命的关系,提出几种相邻轮齿含分度圆裂纹和齿根裂纹的双裂纹齿轮副模型,基于ABAQUS建立齿轮副的三齿啮合有限元分析模型,分析不同载荷作用下分度圆裂纹和齿根裂纹尖端的主应力值和应力强度因子值;结合Pairs方程探讨分度圆裂纹扩展和齿根裂纹扩展寿命之间的关系。结果表明:齿轮副单齿啮合时的裂纹尖端应力比齿轮副双齿啮合时的裂纹尖端应力大,而且裂纹尖端的弯曲应力明显大于剪切应力;同一载荷同一裂纹深度时,齿根裂纹尖端的应力强度因子值大于分度圆裂纹尖端的应力强度因子值;相同加载时,含齿根裂纹齿轮的裂纹扩展寿命小于含分度圆裂纹齿轮的裂纹扩展寿命;裂纹扩展过程中,齿根裂纹深度和分度圆裂纹深度之比非定值,而且深度之比与载荷无关。     

Hilbert-Huang变换在齿轮裂纹故障诊断中的应用

提出一种基于Hilbert-Huang变换的齿轮裂纹故障诊断的新方法。Hilbert-Huang变换是先把时间序列信号,用经验模态分解方法分解成不同特征时间尺度的固有模态函数,然后经过Hilbert变换获得信号时频分布的一种信号处理新方法,将Hilbert-Huang变换应用于齿轮箱中齿轮故障诊断的研究。齿轮故障实验信号的研究结果表明,Hilbert-Huang变换时频分析方法能有效诊断齿轮的齿根裂纹故障。     

Teager-Huang变换在齿轮裂纹故障诊断中的应用

提出了一种基于Teager-Huang变换的齿轮箱故障诊断方法,该方法综合利用了经验模态分解(empiricalmode decomposition,简称EMD)和Teager能量算子分析技术。由于EMD方法具有自适应的分析能力,首先利用EMD把时间序列信号分解成不同特征时间尺度的固有模态函数,然后用Teager能量算子计算各固有模态函数的瞬时幅值和瞬时频率,得到Teager-Huang变换时频谱。齿轮箱齿轮裂纹故障振动试验信号的研究结果表明:Teager-Huang变换时频谱优于Hilbert-Huang变换时频谱,能有效地识别齿轮的裂纹故障。     

基于高阶累积量的齿轮系统故障检测与诊断

详细讨论高阶累积量(Higher order cumulants,HOC)的特点和性质,分析基于HOC理论的相关信号处理技术,以及在处理随机信号的非线性特性时独特的功能.基于HOC的振动信号分析处理技术,即双谱分析、1.5维谱分析以及双相干谱分析方法,分析了各自特点以及估计算法.在此基础上,将HOC引入到机械传动齿轮系统的故障诊断分析之中, 对系统故障特征进行提取并对故障类型进行区分.对不同大小和不同类型故障的传动系统进行试验测试,采用HOC进行特征提取识别,并对HOC理论实际应用效果作了分析评价,表明HOC是一种较理想有前途的有效方法.     

短齿变位渐开线内齿轮的加工

在一般的工厂中,常常会遇到短齿变位直齿内齿轮的加工问题。由于工厂一般不可能存有非标准短齿插齿刀,而到工具厂联系非标准刀具定货,一是不经济,二是时间长。如果用标准插齿刀来加工短齿内齿轮,就会出现齿根圆能满足尺寸,但分度圆齿槽宽减小;或分度圆齿槽宽满足尺寸,但齿根圆直径过大、切得过深的现象。为了使加工短齿变位内齿轮时,既能满足分度圆齿槽宽度尺寸要求,又不超过规定的齿根圆直径,我们采用把标准插齿刀的顶圆直径沿顶后刀面磨小的方法,满足了加工短齿变位内齿轮的要求。经实     

直齿圆柱齿轮分度圆的裂纹损伤机理研究

针对齿轮分度圆位置长期处于交变载荷状态容易产生疲劳裂纹的问题,综合考虑齿轮、转子和轴承之间的耦合关系,建立了十二自由度齿轮—转子—轴承弯扭耦合非线性动力学模型,并分析了分度圆裂纹对齿轮副时变啮合刚度的影响。采用Runge-kutta法求解了齿轮传动系统的运动微分方程,获得了分度圆裂纹损伤状态下系统的动态响应,研究了系统动态响应中的无量纲参数指标。研究结果表明:当分度圆处存在裂纹损伤时,齿轮系统时—频域上均呈现相应的损伤特征,振动信号序列中的峭度指标、裕度因子、峰值因子和脉冲因子均随裂纹的加深而增大;研究可为分度圆处裂纹损伤的演化机理分析和齿轮故障诊断研究提供理论基础。     

强干扰下基于TMD-SVD和POS-BP网络的变速箱状态识别

针对车辆变速箱工作环境恶劣、故障模式难以识别的问题,在现有方法基础上,提出了一种基于经验模态-小波包结合的二次模态分解(Two-layer-mode decomposition,TMD)和奇异值分解(Singular value decomposition,SVD)特征值提取方法,并结合粒子群(POS)-BP神经网络应用于变速箱故障诊断中.首先,在自行搭建的实验台上采集变速箱正常、滚动体故障、外圈裂纹、齿轮磨损4种典型状态下的振动信号;然后,用EMD分解提取信号前5个IMF分量,由于IMF1频谱依然较复杂,采用小波包继续进行2层分解;最终,由二次模态分解得到8个子序列,构建信号分量矩阵,再提取分量矩阵的奇异值作为特征值,将特征值输入构建好的POS-BP神经网络诊断模型中,根据输出识别变速箱故障类型.分析结果表明,该方法能有效应用于特种车辆变速箱故障诊断,诊断正确率达到92％,为复杂工况下变速箱状态识别提供了一种有效的参考途径.     

基于EEMD和Choi-Williams分布的齿轮故障诊断

针对齿轮箱齿轮故障特征提取过程中,经验模态分解(EMD)存在模态混叠、固有模态函数(IMF)筛分困难以及Wigner-Ville分布(WVD)存在交叉干扰项的问题,提出一种集合经验模态分解(EEMD)和Choi-Williams分布(CWD)相结合的齿轮故障诊断方法。首先,将采集到的齿轮故障信号进行EEMD分解,分解为多个单分量固有模态函数(IMF)的组合;然后,通过相关系数和香农熵准则去除虚假分量并筛选IMF;最后,将筛选出的IMF分量进行CWD表达,结合时频域表现出的频率与等时冲击特性,识别出齿轮故障特征。通过齿轮故障仿真和实验分析,验证了该方法在齿轮箱齿轮故障诊断中的适用性和有效性。     

EEMD和TFPF联合降噪法在齿轮故障诊断中的应用

为了消除噪声对齿轮传动系统故障特征提取的影响,提出了一种基于集成经验模态分解(ensemble empirical mode decomposition,简称EEMD)和时频峰值滤波(time-frequency peak filtering,简称TFPF)相结合的降噪方法。针对TFPF算法在窗长的选择方面受到限制的问题,采用了EEMD方法对其进行改进,使得信号在噪声压制和有效信号保真两方面得到权衡;含噪声的信号经过EEMD分解后,得到一系列频率成分从高到低的本征模态函数(intrinsic mode functions,简称IMFs),计算出各IMFs间的相关系数,判断需要滤波的IMFs。对不同的IMFs选择不同的窗长进行TFPF滤波,把过滤后的IMFs和剩余的IMFs重构得到最终的降噪信号。用模拟仿真信号和齿轮齿根故障信号对该方法进行验证,可见EEMD+TFPF能有效地去除噪声,成功提取齿根裂纹故障特征。     

基于EMD/HHT的齿轮箱齿轮不对中故障信号分析

研究了经验模态分解(简称EMD)技术在齿轮箱故障诊断中的应用,并详细论述了EMD原理,对齿轮故障振动信号(简称故障信号)进行EMD,获取固有模态函数(简称IMF)分量,并通过HHT(Hilbert-Huang)变换,对IMF分量进行解调,对解调信号进行频谱分析,提取故障特征频率。研究结果表明,EMD方法能够有效降低信号的噪声,提高信噪比,突出信号的故障特征。     

基于高阶累积量的齿轮裂纹故障检测与识别

介绍了高阶累积量理论,分析了它提取复杂耦合信号和非线性特性的理论基础。在此基础上将高阶累积量方法应用于机械齿轮故障特征分析诊断之中,对无故障齿轮和故障齿轮受力激振后的振动耦合信号进行了分析和特征提取,从而成功地实现了对不同裂纹类故障的识别与诊断,通过与传统的功率谱等方法的比较,说明了高阶累积量方法在齿轮故障诊断中的可靠性和有效性。     

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.     

利用倒阶次谱和经验模态分解的轴承故障诊断

针对齿轮箱升降速过程中振动信号非平稳的特点,将阶次跟踪分析与希尔波特-黄变换技术相结合,提出了基于倒阶次谱和经验模态分解的滚动轴承故障诊断方法.首先,对齿轮箱加速时测得的瞬态信号进行时域采样,对时域信号进行等角度重采样,转化为角域伪平稳信号,然后对角域信号进行经验模态分解.最后,对包含轴承故障信息的高频固有模态函数进行倒阶次谱分析,就可以提取轴承的故障特征.通过对轴承内圈和外圈故障信号的分析表明,该方法能准确识别轴承的故障类型和部位.     

Time-varying frequency-shifting signal-assisted empirical mode decomposition method for AM–FM signals

A time-varying frequency-shifting signal-assisted empirical mode decomposition (EMD) method is presented in this paper. For AM–FM signals, if the extrema density (average number of extrema per unit length) is higher, the frequency is higher approximately, and vice versa. The assisted signal in this method is directly derived from the local extrema using the least square method, and avoids the difficulty that masking signals are not adaptively examined so that the masking signal may not always enable the EMD to generate true mono-component intrinsic mode functions (IMFs). Therefore, the constructed assisted signal is adaptive and suitable for the analysis of frequency-varying components. Compared with the traditional EMD, the method mainly solves the following problems of the traditional EMD: the traditional EMD fails to separate the modes whose frequencies are time-varying and lie within an octave, and fails to remove the time-varying frequency mode-mixing. The results of three experiments are given to show our method's performance and efficiency.     

基于中值滤波-SVD和EMD的声发射信号特征提取

针对随机噪声和脉冲干扰对经验模态分解(EMD)质量的影响,提出中值滤波和奇异值分解(SVD)联合降噪方法,并将其与EMD分解相结合形成一种新的声发射(AE)信号特征提取方法.首先对原始AE信号进行中值滤波,去除幅值较大的异常值;其次对去除异常值的信号序列进行相空间重构和SVD分解,并针对难以确定重构阶数这一问题,提出奇异值能量差分谱概念,利用谱峰的较大值位置来确定重构阶数,以进一步降噪;最后对降噪信号进行EMD分解,以本征模态函数(IMF)的能量占比作为表征各损伤信号的特征向量.数值仿真和5层胶合板损伤的实测数据表明,该方法不仅能够滤除噪声干扰,提高EMD分解的时效性和准确性,而且能够有效地提取出胶合板AE信号特征,对其损伤类型进行有效地识别.     

The application of energy operator demodulation approach based on EMD in machinery fault diagnosis

An energy operator demodulation approach based on EMD (Empirical Mode Decomposition) is proposed to extract the instantaneous frequencies and amplitudes of the multi-component amplitude-modulated and frequency-modulated (AM-FM) signals. Furthermore the proposed approach is applied to machinery fault diagnosis. Firstly, EMD method is used to decompose a multi-component AM-FM signal into a number of intrinsic mode functions (IFMs). Secondly, the energy operator demodulation method is applied to each IMF and the instantaneous amplitudes and frequencies of a multi-component AM-FM signal are extracted. Finally, the spectrum analysis is applied to each instantaneous amplitude in order to obtain envelope spectra from which the mechanical fault can be diagnosed. The analysis results show that the energy operator demodulation approach based on EMD can extract the characteristic of machinery fault vibration signals efficiently.     

采用镜像延拓和RBF神经网络处理EMD中端点效应

在分析经验模态分解端点效应出现原因的基础上,采用镜像延拓法和径向基函数神经网络预测法对端点效应进行了研究,并对一组数值仿真信号和12层钢筋混凝土框架模型振动台试验实测得到的加速度信号进行了边界处理和经验模态分解。算例结果表明,这两种方法基于边界两端预测数据,都可以有效抑制端点效应对分析信号的影响,提高经验模态分解的效果。另外,对于复杂信号仅采用径向基函数神经网络延拓原始信号,对抑制端点效应的效果不很明显,而对复杂信号经滤波后先利用径向基函数神经网络预测、再利用镜像延拓进行处理,则可以明显抑制端点效应的影响。     

均值优化经验模态分解及其在转子故障诊断中的应用

经验模态分解（Empirical mode decomposition,EMD）作为一种自适应的信号分解方法已经被广泛应用于诸多工程领域。为了提高EMD的分解性能,分别考虑从不同权值均值曲线的迭代筛分结果中选择正交性最小以及从每层内禀模态函数迭代结果中选择最优以保证整体分解最优,发展了两种均值优化经验模态分解（Mean-optimized empirical mode decomposition,MOEMD）算法。通过仿真信号分析,将MOEMD方法与EMD等现有信号分解方法进行了对比,结果表明,MOEMD方法在分解性能和分解精度方面比EMD等方法有显著提高。最后,将MOEMD方法应用于转子碰摩故障信号分析,并与EMD进行了对比分析,结果表明,MOEMD方法不仅能够有效地识别转子碰摩故障,而且识别效果优于EMD方法。     

基于经验模态分解的旋转机械振动信号滤波技术研究

把经验模态分解方法引入了旋转机械振动信号滤波领域,详细地解释了这种方法及其算法。通过对一个带高频毛刺干扰的振动信号的经验模态分解与本征模函数的组合,演示了基于经验模态分解的振动信号滤波的方法。把振动信号滤波前后的波形进行了比较,发现基于经验模态分解的信号滤波方法对振动信号滤波有较好的效果。     

Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Biomedical signals are relentlessly superimposed with interferences. The nonlinear processes which generate the signals and the interferences regularly exclude or limit the usage of classical linear techniques, and even of wavelet transforms, to decompose the signal.Empirical Mode Decomposition (EMD) is a nonlinear and adaptive technique to decompose data. Biomedical data has been one of its most active fields. EMD is fully data-driven, thus producing a variable number of modes. When applied to cardiovascular signals, the modes expressing cardiac-related information vary with the signal, the subject, and the measurement conditions. This makes problematic to reconstruct a noiseless signal from the modes EMD generates.To synthesize and recompose the results of EMD, Principal Component Analysis (PCA) was used. PCA is optimal in the least squares sense, removing the correlations between the modes EMD discovers, thus generating a smaller set of orthogonal components. As EMD-PCA combination seems profitable its impact is evaluated for non-invasive cardiovascular signals: ballistocardiogram, electrocardiogram, impedance and photo plethysmogram.These cardiovascular signals are very meaningful physiologically. Sensing hardware was embedded in a chair, thus acquiring also motion artefacts and interferences, which EMD-PCA aims at separating. EMD is seen to be important, because of its data adaptability, while PCA is a good approach to synthesize EMD outcome, and to represent only the cardiovascular portion of the signals.