基于时变滤波经验模态分解的轴承故障诊断

针对经验模态分解(empirical mode decomposition,EMD)由于模态混叠现象难以有效提取轴承故障特征的问题,提出了一种基于时变滤波经验模态分解(time varying filtering based empiricalmode decomposition,TVF-EMD)的轴承故障诊断方法。首先利用TVF-EMD方法对轴承故障信号进行自适应分解,得到一组内禀模态函数(intrinsic mode functions,IMFs),然后根据峭度最大准则选取包含主要故障特征信息的IMF分量,最后对选取的敏感分量进行进一步的包络解调分析,提取出故障特征信息,从而进行故障诊断。轴承故障诊断实例证实了所提方法能准确提取轴承故障的特征信息,实现轴承故障的有效诊断;通过与总体经验模态分解(ensemble empirical modedecomposition,EEMD)方法的对比研究,表明了所提方法的优越性。     

改进小波去噪Teager算子的齿轮微弱故障提取方法

针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(HilbertHuang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。     

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。     

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。     

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。     

基于k值优化VMD的滚动轴承故障诊断方法

针对旋转机械中滚动轴承早期信噪比较低的故障特征提取困难问题,提出了一种基于能量的变分模式分解(variational mode decomposition,简称VMD)模态数k优化选取方法,用以提取滚动轴承早期故障特征,同时避免了信号分解过分或不足。首先,对振动信号进行VMD预分解,分别在不同k值条件下计算分量信号能量与原始信号总能量;其次,根据基于能量的模态数k选取准则,确定最佳模态数值对信号进行VMD分解;最后,通过峭度准则选择分量进行信号重构,对其进行包络分析,提取故障特征频率。将该方法运用到实际故障信号中,有效提取出滚动轴承内圈微弱故障特征,实现了早期故障特征判别,具有一定的应用价值和实际意义。     

一种改进的LCD滚动轴承故障特征提取方法

为了有效提取出滚动轴承振动信号的故障特征,提出一种基于分段三次Hermite插值?局部特征尺度分解PCHIP?LCD的故障特征提取方法.采用分段三次Hermite插值代替LCD中的三次样条插值(Cubic spline interpolation,CSI),提出了改进的LCD方法,完成振动信号x(t)分解,获得若干内禀尺度分量(Intrinsic scale components,ISCs);建立基于峭度和相关系数K?C组合权重指标的有效ISCs分量筛选规则,完成ISCs分量的选择和重构,获得重构信号xnew(t);对重构信号xnew(t)进行Teager能量算子(Teager energy operator,TEO)解调分析,获得TEO解调能量谱图,进而实现滚动轴承的故障特征提取.结果表明,该方法可增强对信号的包络拟合能力,提高信号的分解精度.     

随机共振降噪下的齿轮微弱故障特征提取

针对强背景噪声下的齿轮微弱故障特征提取问题,提出了一种将级联单稳随机共振与经验模式分解(EMD)-Teager能量算子解调方法相结合的特征提取方法。首先对含噪故障信号进行随机共振输出,降噪后再进行经验模式分解,分解得到具有不同特征时间尺度的固有模态函数(IMFs),最后通过Teager能量算子解调方法求取每个有效IMF分量的幅频信息,从而提取齿轮微弱故障特征。仿真分析和实际测试结果均表明,通过随机共振降噪后,该方法能有效检测出齿轮局部损伤故障特征频率。     

基于迭代滤波和MOMEDA的滚动轴承故障诊断方法

针对噪声环境下轴承故障微弱特征难以提取的问题,提出了一种基于迭代滤波和多点最优最小熵反褶积相结合的轴承故障特征提取方法。该方法首先运用迭代滤波分解方法将滚动轴承振动信号进行分解,得到多个本征模态分量,然后运用相关系数和峭度确定最能体现轴承故障信息的敏感分量,最后对敏感分量进行多点最优最小熵反褶积消噪处理后进行频谱分析,从而提取轴承故障特征。通过数值仿真信号的分析和内圈故障信号的分析验证了所提出方法的有效性。     

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。     

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。     

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分量筛选方法;对筛选出的IMF分量,计算其解析能量谱样本熵作为特征参数;在分析二叉树SVM缺点基础上,建立了投票法SVM多分类器;经试验验证,基于Teager能量谱样本熵的故障诊断准确率为78%,而基于解析能量谱样本熵的故障诊断准确率为100%,充分证明了解析能量算子在轴承故障诊断中的有效性。     

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。