基于ITD-形态滤波和Teager能量谱的轴承故障诊断

针对强背景噪声下滚动轴承振动信号故障特征信息难以提取的问题,提出了结合固有时间尺度分解(ITD)-形态滤波和Teager能量谱的滚动轴承故障特征提取与诊断方法。首先对滚动轴承振动信号采用ITD方法分解,得到若干个固有旋转分量;考虑到噪声主要分布在高频段,取前2个高频的固有旋转分量进行形态滤波,并将滤波后的信号与剩余固有旋转分量重构;对重构信号计算Teager能量算子并绘制Teager能量谱,从Teager能量谱中可以识别出故障特征。将本方法应用于滚动轴承的内圈故障和外圈故障诊断,结果表明ITD-形态滤波可以有效去除振动信号中的背景噪声并保留冲击特征,Teager能量谱可以直观并准确显示出故障特征。     

LCD联合改进Teager算子的轴承故障诊断

提出了将局部特征尺度分解(Local characteristic-scale decomposition,简称LCD)和改进Teager能量算子(NTEO)相结合的滚动轴承故障诊断方法。首先简单介绍了近几年新提出的一种自适应时频分析方法 LCD,它能够将一个复杂的多分量信号分解为若干内禀尺度分量(Intrinsic scale component,简称ISC),计算各阶ISC的峭度和与原信号的相关度,然后对峭度和相关度都比较大的ISC用NTEO计算瞬时Teager能量序列,接下来把各阶Teager能量序列相加得到总的能量序列,最后对能量序列做快速傅里叶变换,查找故障频率。分别把有内圈和外圈故障的轴承的振动信号进行了分析,有效的提取出了故障特征频率,并与传统的Hilbert包络谱方法和Teager能量谱进行对比,验证了方法的优越性。     

基于改进型CEEMDAN-TKEO滚动轴承故障诊断方法研究

针对滚动轴承故障识别困难这一问题,提出了基于改进型CEEMDAN和Teager能量算子(TKEO)的诊断方法。首先,将传感器测得的故障振动信号采用CEEMDAN改进算法分解,得到多个固有模态函数(IMF),此过程可以削弱噪声成分的干扰,增强故障特征;然后,计算最具相关性模态信号的Teager能量算子并进行包络谱分析,通过谱中的频率成分实现故障诊断。实验结果表明,基于改进型CEEMDAN和Teager能量算子的诊断方法能够有效提取轴承故障信号中的微弱特征信息,具有一定的工程实用价值。     

一种改进的暂态电能质量扰动检测方法研究

针对希尔伯特-黄变换算法(HHT)在检测电能质量扰动时端部存在失真及瞬时幅值分量不稳定等问题,提出了一种基于局部均值分解法(LMD)和Teager能量算子的暂态电能质量扰动检测算法,采用LMD算法分解扰动信号,提取所需能量信号,然后利用Teager能量算子法对该能量信号进行检测。最后,以不同的扰动信号为研究对象进行了试验,验证了改进算法的有效性。     

基于HVD与1.5维Teager能量谱的滚动轴承故障诊断方法研究北大核心

针对强背景噪声下滚动轴承微弱故障信号特征难以提取的特点,提出了一种基于Hilbert振动分解(HVD)与1.5维Teager能量谱的滚动轴承故障诊断方法。首先将故障信号进行HVD分解,然后采用相关系数法重构原始信号,降低噪声的干扰,增强故障信号的冲击特征,最后对重构后的信号进行1.5维Teager能量谱运算,提取出滚动轴承的故障特征。     

二维Teager滤波器及其在图像处理中的应用研究

二维Teager滤波器是一种非线性边缘提取滤波器,是均衡二次Volterra滤波器的一种特殊情况,其特性近似于Mean-Weighted高通滤波器,即兼有局域平均和高通滤波的特性,因而可均衡去除噪声和增强图像边缘.文中首先分析了一维Teager滤波器的特性,并得到了Teager滤波器的二维模型.最后将二维Teager滤波器应用在图像处理中,取得了满意的实验效果.     

基于HVD与1.5维Teager能量谱的滚动轴承故障诊断方法研究

针对强背景噪声下滚动轴承微弱故障信号特征难以提取的特点,提出了一种基于Hilbert振动分解(HVD)与1.5维Teager能量谱的滚动轴承故障诊断方法。首先将故障信号进行HVD分解,然后采用相关系数法重构原始信号,降低噪声的干扰,增强故障信号的冲击特征,最后对重构后的信号进行1.5维Teager能量谱运算,提取出滚动轴承的故障特征。     

SVD-LMD联合降噪和TEO的滚动轴承故障诊断

针对随机噪声背景下滚动轴承局部损伤信息提取困难的问题,提出了一种奇异值分解(Singular value decomposition,SVD)和局部均值分解(Local mean decomposition,LMD)联合降噪,并结合Teager能量算子(Teager energy operator,TEO)的特征提取新方法.首先,利用SVD方法对滚动轴承故障振动信号进行处理,初步剔除背景噪声;然后,使用LMD方法分解降噪后的信号,依据相关系数指标筛分出敏感乘积函数(Product function,PF)并加以重构;最后,对重构的信号进行TEO解调分析,将解调谱中幅值突出的频率成分与故障特征频率理论值进行对比,提取故障信息.结果表明,该方法可有效提取轴承局部损伤的特征频率,最终实现故障诊断.     

面向智能机器人的Teager语音情感交互系统设计与实现

自从Maragos首先提出了Teager能量算子(TEO)后,该算子得到了一系列应用.Teager能量算子的非线性特性,使之能在抑制背景噪声的同时进行信号特征的有效提取.它还保留了倒谱分析方法中的准稳态假设,因而更能有效地表征语音信号的复杂性.探索了基于Teager能量算子的非线性特征对于区分情感语音的有效性,并且进一步将其和普通的声学参数和音质参数相结合,以应用于语音情感的识别中.以HMM和GMM为分类器,选取7种情感设计了4组对比实验,达到最高85％的识别正确率.实验结果表明,将传统的语音情感特征和Teager能量算子的非线性特征相结合,能显著地提高语音情感识别系统的性能.在此基础上,提出了智能情感机器人进行情感交互的框架,设计实现了智能服务机器人的情感交互系统,经过实时情感交互实验验证,该智能服务机器人可以很好地与交互对象进行简单的情感交流.     

基于LMD和平滑Teager能量算子解调的电机滚动轴承故障特征提取

作为电机转子的支撑元件,电机轴承故障在电机故障中占有很大比例。针对电机滚动轴承振动信号多分量调幅调频的特点,提出一种基于局域均值分解(LMD)和平滑Teager能量算子的电机轴承故障特征提取方法。该方法首先通过LMD将多分量调制信号分解为若干个单分量调制信号,再运用平滑Teager能量算子对包含主要故障信息的分量进行解调,从而准确地分析出轴承的故障特征。模拟和实例证明了该方法的有效性。     

Early fault feature extraction of bearings based on Teager energy operator and optimal VMD

As the fault shock component in vibration signals is extremely sparse and weak, it is difficult to extract the fault features when large-scale, low-speed and heavy-duty mechanical equipment is in the early stage of failure. To solve this problem, an early fault feature extraction method based on the Teager energy operator, combined with optimal variational mode decomposition (VMD) is presented in this study. First, the Teager energy operator was used to strengthen the weak shock component of the original signal. Next, a logistic–sine complex chaotic mapping with variable dimensions was constructed to enhance the global search ability and convergence speed of the pigeon-inspired optimization (PIO) algorithm, which is named the variable dimension chaotic pigeon-inspired optimization (VDCPIO) algorithm. Then, the VDCPIO algorithm is used to search for the optimal combination value of key parameters of VMD. The enhanced vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by the optimized VMD, and then kurtosis for every IMF and mean kurtosis of all IMFs are extracted. According to the average kurtosis, several IMFs, whose kurtosis value is greater than the average kurtosis value, are selected to reconstruct a new signal. Then, envelope spectrum analysis of the reconstructed signal is carried out to extract the early fault features. Finally, experimental verification of the method was performed using the simulated signal and measured signal from a rolling bearing; the experimental results indicate that the method presented in this paper is more effective to extract the early fault features of this kind of mechanical equipment.     

基于SVD-EEMD和TEO的滚动轴承弱故障特征提取

将奇异值分解(singular value decomposition,简称SVD)与集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)进行结合,提出一种适用于滚动轴承弱故障状态描述的敏感特征提取方法。为提高信号故障信息的提取质量,对采集信号进行相空间重构得到一种Hankel矩阵。根据该矩阵的奇异值差分谱,确定降噪阶次进行SVD降燥。用EEMD分解降噪后的信号可获得11个本征模态函数(intrinsic mode function,简称IMF)和1个余项。依据建立的峭度-均方差准则,筛选出一个能够有效描述故障状态的敏感IMF分量,计算其相应的Teager能量算子(Teager energy operator,简称TEO),对此TEO进行Fourier变换,实现了对滚动轴承弱故障模式的有效辨识。用美国凯斯西储大学公开的滚动轴承故障信号对所建立的方法与传统EEMD-Hilbert法和EEMD-TEO方法进行对比,结果表明:经本方法提取的敏感特征能准确突显滚动轴承故障频率发生的周期性冲击,可准确识别其故障类型。     

Fault feature extraction of a rotor system based on local mean decomposition and Teager energy kurtosis

Feature extraction is the most important step for machine fault diagnosis, but useful features are very difficult to extract from the vibration signals, especially for intelligent fault diagnosis based on data-driven technique. An integral method for fault feature extraction based on local mean decomposition (LMD) and Teager energy kurtosis (TEK) is proposed in this paper. The raw vibration signals are first processed via LMD to produce a group of product functions (PFs). Then, the Teager energies are computed using the derived PFs. Subsequently, each Teager energy data set is directly used to calculate the corresponding TEK. A vibration experiment was performed on a rotor-bearing rig with rub-impact fault to validate the proposed method. The experimental results show that the proposed method can extract different TEKs from the mechanical vibration signals under two different operating conditions. These TEKs can be employed to identify the normal and rub-impact fault conditions and construct a numerical-valued machine fault decision table, which proves that the proposed method is suitable for fault feature extraction of the rotor-bearing system.     

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.     

机械加工过程中轴承故障诊断方法研究

为了提高机械加工过程中滚动轴承故障诊断准确度,提出了基于新的解析能量算子的轴承故障诊断方法.在分析Teager能量算子缺陷基础上,提出了新的能量算子,命名为解析能量算子;解析能量算子无需满足Teager能量算子的使用条件,且能够更好地跟踪故障信号的冲击瞬态特征;使用EMD算法分解原始振动信号,给出了多指标融合的IMF分...     

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。     

基于WAEEMD和MSB的滚动轴承故障特征提取

针对调制信号双谱（MSB）方法仅能处理平稳信号的不足,提出了一种基于加权平均集成经验模态分解（WAEEMD）和MSB的滚动轴承故障特征提取方法。首先,利用WAEEMD将滚动轴承的非平稳振动信号分解成一系列具有平稳特性的固有模态函数（IMF）;然后,开发了一种基于Teager能量峭度（TEK）的加权平均方法以强调敏感IMF的重要性,并将加权后的IMF重构为WAEEMD滤波信号;最后,应用MSB分解WAEEMD滤波信号中的调制分量并提取故障特征频率。仿真和实验结果表明,相对于快速谱峭度（FK）和EEMD-MSB方法,WAEEMD-MSB方法能更准确地获取故障特征,从而验证了WAEEMD-MSB方法的有效性。     

基于参数自适应特征模态分解的滚动轴承故障诊断方法

针对强背景噪声下轴承故障信息难以有效提取的问题,提出一种基于参数自适应特征模态分解的滚动轴承故障诊断方法。首先,为了克服原始特征模态分解(FMD)需要依赖人为经验设定关键参数而不具有自适应性的缺点,提出基于平方包络谱特征能量比(FER-SES)的网格搜索方法自动地确定FMD的模态个数n和滤波器长度L;随后,采用参数优化的FMD将原轴承振动信号划分为n个模态分量,并选取具有最大FER-SES的模态分量为敏感模态分量;最后,通过计算敏感模态分量的平方包络谱来提取故障特征频率,从而判别轴承故障类型。通过仿真信号和工程案例分析验证了提出方法的有效性。与变分模态分解(VMD)和谱峭度方法(SK)相比,提出方法具有更好的故障特征提取性能。