基于LMD的能量算子解调机械故障诊断方法

为了提取多分量调幅调频信号的幅值和频率信息,提出了基于局部均值分解(local mean decomposition,简称LMD)的能量算子解调机械故障诊断方法.该方法先利用LMD将机械调制信号分解成若干个乘积函数(production function,简称PF)分量,然后对每一个PF分量进行能量算子解调,获得信号的幅值和频率信息进行故障诊断.利用该方法对仿真信号以及轴承和齿轮故障振动信号进行实验研究的结果表明,基于LMD的能量算子解调方法能够有效地提取机械故障振动信号特征.     

基于对称差分能量算子解调的局部均值分解端点效应抑制方法

局部均值分解(Local mean decomposition,LMD)在分析非线性、非平稳信号时表现出特有的分析能力,能够有效获得非平稳信号的时频特征,但是局部均值分解过程中的端点效应会导致分解结果失真,针对这一问题,从振动信号解调分析角度出发,提出基于对称差分能量算子解调的局部均值分解端点效应抑制方法,采用局部均值分解方法将原信号分解为一系列单分量信号,然后对每一个单分量信号进行三点对称差分能量算子解调,得到各单分量信号的瞬时幅值和瞬时频率,从而获得原信号的时频分布。为评价该端点效应抑制方法的抑制效果,定义一种评价标准,通过与其他两种端点效应抑制方法进行比较,验证该方法的优越性。仿真和试验结果表明该方法能够有效抑制LMD端点效应,实现旋转机械故障的有效诊断。     

基于EMD的能量算子解调方法及其在机械故障诊断中的应用

为了提取多分量的AM-FM信号的频率和幅值信息,提出了基于EMD (Empirical mode decomposition)的能量算子解调法,并将它应用于机械故障诊断中。该方法首先采用EMD将多分量的AM-FM信号分解成若干个IMF(Intrinsic mode function)分量之和,然后对每一个IMF分量进行能量算子解调,从而提取多分量的AM-FM信号的幅值和频率信息。对机械故障振动信号的分析结果表明,基于EMD的能量算子解调法能有效地提取机械故障振动信号的特征。     

基于VMD和对称差分能量算子解调的滚动轴承故障诊断方法

针对滚动轴承早期故障振动信号非平稳、强噪声,故障频率难提取的问题,提出了基于变分模态分解(Variational Mode Decomposition,VMD)和对称差分能量算子解调的滚动轴承故障诊断方法。首先,利用VMD方法将滚动轴承待分析信号分解成若干个模态分量;其次,根据峭度最大准则来选取被对称差分能量算子解调的模态分量,解调后获取待分析信号的幅值、频率信息并计算包络谱。实验结果表明:与传统能量算子相比,所提方法能突显故障特征频率并有效抑制虚假干扰频率,更有利于滚动轴承故障诊断。     

基于小波包熵与EMD的能量算子解调机械故障诊断

针对在强噪声背景下轴承振动信号的非线性,非平稳性以及调制源微弱难以提取故障特征的问题,提出了一种基于小波包熵值与EMD(经验模态分解)结合的能量算子解调故障诊断方法。该方法首先对信号进行小波包熵值降噪,进而选取相关度最大的IMF(本征模态分量)进行能量算子解调,从而实现了提取该分量下的故障信号的幅值和频率信息。对机械故障振动信号进行实验分析表明,相对于普通Hilbert解调法的运算精度与运算速度满足不了诊断需求的情况下,该方法能够有效解调出故障频率信息,实现对故障类别的推断。     

基于EMD与自相关的能量算子解调机械故障诊断

针对在强噪声背景下早期轴承故障振动信号的非线性,非平稳性以及信号出现的复杂调制现象,提出一种基于EMD与自相关函数相结合的能量算子解调故障诊断方法。该方法首先根据信号的小波包熵值对信号小波包降噪,而后降噪信号进行EMD分解,提取相关度最大的IMF分量进行自相关函数分析的方法进一步抑制噪声对提取特征频率的干扰,然后对降噪处理过的信号进行能量算子解调,从而实现提取轴承的故障信号的幅值和频率信息。对机械故障振动信号进行实验分析表明,相对于单纯的小波包分析预处理存在的降噪效果不理想以及普通Hilbert解调法的运算精度满足不了诊断需求的情况,该方法能够有效解调出故障频率信息,以实现对故障类别的推断。     

归一化复域能量算子解调及其在转子碰摩故障诊断中的应用

归一化希尔伯特变换(Normalized Hilbert transform,NHT)解调采用经验AM-FM分解实现信号的包络信号(即瞬时幅值)和纯调频信号的分离,再对纯调频信号进行希尔伯特变换提取瞬时频率。与直接希尔伯特变换解调比较,归一化希尔伯特变换的解调效果有较大提高。然而,研究发现,经验AM-FM分解得到的纯调频信号可能存在易导致负频率出现的骑波,并且由于归一化希尔伯特变换求取瞬时频率仍采用希尔伯特变换,则不可避免地在端点处产生振荡。针对归一化希尔伯特变换解调存在的问题,提出可以消除骑波的改进的经验AM-FM分解以及基于复域能量算子的纯调频信号的瞬时频率估计,并在此基础之上进一步提出一种新的信号解调方法——归一化复域能量算子(Normalized complex Teager energy operator,NCTEO)解调,采用改进的经验AM-FM分解提取单分量信号的瞬时幅值,再用基于复域能量算子的瞬时频率估计对纯调频信号进行解调提取瞬时频率。通过仿真试验以及转子早期碰摩故障诊断的应用实例验证了归一化复域能量算子解调的优越性和有效性。     

基于线调频小波路径追踪阶比能量解调的齿轮轮齿裂纹故障诊断

针对变速下齿轮裂纹故障信号微弱,难以提取这一特点,提出了基于线调频小波路径追踪的阶比能量解调算法,并将其应用于变速下的齿轮裂纹故障诊断。该方法先采用线调频小波路径追踪算法提取齿轮的啮合频率分量,由此得到转速信号;然后利用转速信号对原始信号进行等角度采样得到角域平稳信号;接着对角域平稳信号进行带通滤波和角域平均运算以消除干扰噪声的影响;最后使用能量算子解调求取瞬时频率和瞬时幅值,根据瞬时频率和瞬时幅值进行故障诊断。应用实例表明,该方法能有效地提取变速下的齿轮裂纹故障。     

基于迭代Hilbert变换的多分量信号解调方法研究及应用

旋转机械系统发生故障时,其振动信号通常为多分量AM-FM信号。针对传统的解调方法在多分量振动信号故障特征提取中的局限性,提出一种利用迭代Hilbert变换(Iterated Hilbert transform,IHT)进行机械故障诊断的新方法。介绍IHT的基本原理;通过对任一两分量的AM-FM信号的分析表明利用IHT得到的相位信息直接估计瞬时频率具有一定的局限性,于是提出基于差分算和零相位数字低通滤波的平滑的瞬时频率估计方法,并通过仿真试验表明,与自适应分割算法和Hilbert-Huang变换相比,该方法具有很高的精度且速度较快。对具有外圈故障的滚动轴承和具有断齿故障的齿轮箱振动信号的分析结果表明,基于IHT的多分量AM-FM信号解调方法能有效地提取机械故障振动信号中的故障特征。     

基于局部特征尺度分解的经验包络解调方法及其在机械故障诊断中的应用

提出一种基于局部特征尺度分解(Local characteristic-scale decomposition, LCD)和经验包络(Empirical envelope method, EE)解调的非平稳信号分析方法。该方法通过局部特征尺度分解将一个复杂信号自适应地分解为若干个内禀尺度分量之和,对得到的各个内禀尺度分量进行经验包络解调,得到各个分量信号的瞬时幅值和瞬时频率信息,从而得到原始信号完整的时频分布。采用仿真信号将基于LCD和EE解调的时频分析方法和希尔伯特黄变换方法进行对比,结果表明,新提出的信号分解和解调方法在抑制端点效应和迭代所需时间,瞬时特征的精确性等方面优于希尔伯特黄变换方法。针对滚动轴承和齿轮故障振动信号的调制特点,将基于LCD和EE的时频分析方法引入机械故障诊断中,对试验信号的分析结果表明,基于LCD和EE的时频分析方法能有效地提取机械故障振动信号的特征。     

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.     

OEEMD与Teager能量算子结合的轴承故障诊断

针对滚动轴承发生局部故障时振动信号中微弱周期性冲击的特征提取问题,提出参数优化集合经验模式分解(optimal ensemble empirical mode decomposition,简称OEEMD)与Teager能量算子解调结合的滚动轴承故障诊断方法。首先,针对集合经验模式分解(ensemble empirical mode decomposition,简称EEMD)过程中两个关键参数k(加入白噪声的幅值系数)和m(集合平均次数)的准确选取问题,通过引入相关系数、相关均方根误差和信噪比分析,给出一种可自适应确定这两个参数取值的OEEMD方法,通过OEEMD将冲击从滚动轴承振动信号中分离出来;其次,采用Teager能量算子对其进行包络解调,计算出瞬时幅值后再对瞬时幅值进行包络谱分析,以获取冲击的特征频率,从而对滚动轴承故障进行准确诊断。仿真信号分析和应用实例验证了该方法的有效性。     

An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection

Bearings are among the most frequently used components. Bearing failure could lead to complete stall of a mechanical system, unpredicted productivity loss for production facilities or catastrophic consequence for mission-critical equipment. As such, bearing fault detection and diagnosis is an imperative part of most of preventive maintenance procedures. This paper presents a parameter independent yet simple to implement fault detection technique. The Teager energy operator is tailored to extract both the amplitude and frequency modulations of the vibration signals measured from mechanical systems. The incorporation of the frequency modulation information into the proposed bearing fault detection method has eliminated the need for interference removal steps. As the amplitude demodulation (AD) is also inherent in the energy operator, the fault frequency can be detected from the spectrum of the energy-transformed signal. The effectiveness of the proposed method has been validated using both simulated and experimental data.     

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented. This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique. The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency modulation (AM-FM) intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.     

变分非线性单分量 Chirp 模态提取及其在电机轴承诊断中的应用

变频驱动下旋转机械设备的振动信号具有调制成分复杂、涉及频带较宽和噪声干扰严重等问题,造成与故障相关的单分量调制成分提取困难。为此,提出了一种新的变分非线性单分量chirp模态提取(VNSCME)方法,建立单个目标模态解调频带最窄与残余分量能量最小组合约束的变分优化模型,迭代提取特定的单分量非线性调制成分。预设一个有关目标模态瞬时频率的先验知识,VNSCME能够独立地提取出特定的单分量调制成分并准确估计其瞬时频率。与现有研究相比,VNSCME具有不受时频分布分辨率限制、初始化简单和计算效率高的特点。将VNSCME与阶次跟踪技术相结合,应用于变频驱动电机轴承故障诊断。分别对仿真和实测的故障振动信号进行处理,结果显示瞬时转频估计的相对误差低于0.76%,提取单个目标模态的计算时间低于11.9 s,验证了所提方法的有效性。     

基于改进经验小波变换的行星齿轮箱故障诊断

行星齿轮箱振动信号具有复杂多分量和调幅-调频的特点。幅值解调和频率解调方法能够避免传统Fourier频谱中的复杂边带分析,有效识别故障特征频率。经验小波变换通过对信号Fourier频谱的分割构造一组正交滤波器组,能提取具有紧支撑Fourier频谱的单分量成分,再对单分量成分运用Hilbert变换即可实现信号的解调分析。经验小波变换能够有效分离出调幅-调频成分,不存在模态混叠现象,具有完备的理论基础,自适应性好、算法简单、计算速度快。将改进的经验小波变换应用于行星齿轮箱振动信号的解调分析;提出了一种单分量个数的估算方法,解决了经验小波变换中的Fourier频谱划分问题;给出了对故障敏感的信号分量的选取方法,提高了分析的针对性。将改进方法应用于行星齿轮箱振动仿真信号和实验信号分析,验证了该方法的有效性。     

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.     

Research on iterated Hilbert transform and its application in mechanical fault diagnosis

Multicomponent AM–FM demodulation is an available method for machinery fault vibration signal analysis, so a new method for mechanical fault diagnosis based on iterated Hilbert transform (IHT) is proposed. The principle of computing the asymptotically exact multicomponent sinusoidal model for an arbitrary signal by iterating Hilbert transform is introduced, and some properties of IHT are analyzed. Theoretical analysis for the generic two-component signal shows that there are limitations in the direct estimation of instantaneous frequencies via the phase signals of the previously obtained model. Therefore, a smoothed instantaneous frequency estimation (SIFE) method based on difference operator and zero-phase digital low-pass filtering is proposed, and then the accuracy and validity of this method have been proved by the simulation results. The analysis results of the mechanical fault signals show that the weak features of these signals can be efficiently extracted with the proposed approach.     

结合复小波包变换及频谱校正的机械转子碰摩故障诊断方法研究

针对机械转子系统中碰摩故障发生时故障特征微弱及识别困难的问题,提出一种结合双树复小波包变换及频谱校正的故障诊断方法。首先对于振动位移信号中工频基波成分,采用频谱加矩形窗的频谱校正方法识别其谐波信息,通过构造补偿信号进行对消,以减少其对后续特征提取的影响。其次通过双树复小波包对补偿过的信号进行多尺度分解;最后对小波包子空间信号进行希尔伯特包络解调分析,通过瞬时幅值及瞬时频率信息诊断转子的动静碰摩故障。在转子实验台上进行了实验验证,结果表明提出的方法能有效提取转子碰摩产生的微弱故障特征。