基于EMMD和BSS的单通道旋转机械故障诊断方法

针对在欠定的观测信号情况下,传统基于矩阵的盲源分离算法效果比较差的问题,提出一种基于极值域均值模式分解和盲源分离的单通道旋转机械信号故障特征提取方法,并应用于实际的故障诊断中.该方法先通过极值域均值模式分解法分解观测信号,把得到的固有模态函数和原观测信号一起组成新观测信号,从而实现了信号升维,使欠定问题转化为正定问题;然后,由奇异值分解和贝叶斯准则进行源数估计;最后,利用基于四阶累积量的特征矩阵联合对角化方法实现信号的盲分离.通过仿真,验证了该方法对旋转机械故障信号进行盲源分离的可行性.将提出的方法应用到齿轮和轴承系统的故障诊断中,进一步证明了该方法的有效性.     

基于改进谐波小波包分解的滚动轴承复合故障特征分离方法

为解决滚动轴承单通道振动信号中复合故障特征难以分离的问题,提出了基于改进谐波小波包分解的轴承复合故障特征分离方法。首先,改进了二进谐波小波包分解方法,提出了连续谐波小波包分解方法,克服了信号分解后子带个数和带宽范围受二进制划分的缺陷;然后,采用谐波窗分解提取信号中频率成分集中的频段,根据轴承各单点故障特征频率确定分解层数,进行连续谐波小波包分解,利用能量算子包络解调得到子带信号中各个单点故障的权重因子;最后,重构轴承各单点故障信号,实现复合故障的特征分离和提取。对仿真信号和实测轴承内、外圈复合故障信号分析的结果表明,该方法能将轴承单通道复合故障信号分解到不同的通道中,实现了复合故障特征的分离,具有一定的工程实用价值。     

局部特征尺度分解和局部切空间排列在故障特征频率提取中的应用

为了从非线性、非平稳的振动信号中提取故障特征频率,提出了一种故障特征频率提取新方法。该方法将局部特征尺度分解和流形学习算法局部切空间排列相结合,首先利用局部特征尺度分解将振动信号分解成若干个内禀尺度分量,将其组成多维特征向量;其次采用流形学习算法中的局部切空间排列对多维特征向量进行降维处理,得到低维特征向量,对得到的低维特征向量进行信号重构;最后采用频谱分析方法对重构信号进行故障特征频率的提取。在滚动轴承故障试验中,所提出方法能够准确提取出内圈和外圈故障的特征频率,验证了该方法的有效性。     

基于流形学习的滚动轴承故障盲源分离方法

滚动轴承的故障信号是一种典型的非线性非平稳信号,其信号中常常混有噪声信号及其他干扰成分。提出了一种基于流形学习的滚动轴承故障盲源分离方法,首先,利用经验模态分解(empirical mode decomposition,简称EMD)对单通道模拟信号进行分解,对得到的多通道信号构造其协方差矩阵,计算矩阵的奇异值下降速比得到原始信号数目;其次,利用峭度等指标选择最优观测信号,利用核主成分分析(kernel principal components analysis,简称KPCA)提取信号的流形成分;最后,利用快速独立成分分析(fast independent component analysis,简称Fast ICA)还原得到源信号。该方法不但解决了故障信号的欠定盲源分离问题,还提出了最优观测信号的确定准则,并通过实例验证了方法的有效性。     

正交局部保持投影早期故障特征提取方法

针对轴承早期故障特征难以提取,提出了一种基于正交局部保持投影的轴承故障特征提取方法。由时域指标和小波频带能量组成高维特征空间。运用正交局部保持投影方法通过训练样本数据求出正交转换矩阵,测试样本经正交转换矩阵转化后得到低维向量。利用不同故障样本的类间散度和同种故障样本的类内散度两个指标来衡量该方法的有效性,通过滚动轴承故障数据的仿真,证明提出的正交局部保持投影的特征提取方法是有效的。     

基于EEMD的单通道盲源分离在轴承故障诊断中的应用

针对一维观测矩阵的极度欠定盲分离模型,结合盲源分离和总体经验模式分解的优点,利用总体经验模式分解将单通道信号转化为固有模态矩阵,重组观测矩阵,再通过近似联合对角化实现信号的盲分离。数据仿真说明该方法能提取低信噪比下的轴承故障信息。实验中,对2种不同故障的轴承进行故障诊断,从而进一步证明了该方法的有效性。     

基于正交局部保持映射的转子故障诊断方法

为了改善故障模式识别的分类性能,提出了一种基于正交局部保持映射算法的多流形特征提取方法。对于高维的非线性数据可以有效地提取低维流形特征向量,并且不会改变数据的内在属性。利用转子的振动信号构造一个高维多征兆矩阵,然后在应用正交局部保持映射将这个高维矩阵进行降维,提取低维特征向量矩阵,映射在可视空间里,从而可以有效地达到故障分类的效果,提高故障诊断的准确率。最后通过实验和数据降维仿真证明了正交局部保持映射算法的有效性和可行性。     

基于β散度约束的非负矩阵分解的机械复合故障诊断方法

时频分析经常被用来刻画非平稳振动信号的局部信息,而经时频变换后的特征信号具有较高的矩阵维数,很难对高维特征矩阵直接进行分类或特征提取.为此,提出了基于时频分析与β散度约束的非负矩阵分解算法(NMF)相结合的机械复合故障诊断方法.对采集的振动信号进行时频分析,获取局部特征信息;利用β-NMF算法实现数据的降维,并根据特征信息重构信号;在β-NMF算法中引入加权脉冲因子(CIF),对重构后的信号进行筛选;将得到的分离信号进行包络频谱分析,实现故障诊断.以滚动轴承复合故障为研究对象进行验证,分析结果表明:所提出的方法可以有效提取出外圈与滚动体冲击性特征,实现了滚动轴承的复合故障诊断.     

基于IWT-CEEMD-cICA的滚动轴承故障诊断方法

为在强噪声下准确利用振动信号进行轴承微弱故障的诊断,提出了一种基于改进小波阈值、互补集合经验模态分解和约束独立分量分析的故障诊断方法。首先,对单通道振动信号进行改进小波阈值降噪预处理,提高输入信号的信噪比;然后,进行CEEMD处理以实现降噪及单通道扩展,基于峭度值和相关系数选取有效固有模态函数并将其作为盲源分离的输入信号;最后,通过cICA方法提取目标振动信号,识别故障特征。     

基于VMD-LPP融合的轴承故障特征频率提取

为提高轴承故障特征频率的提取效果,提出了变分模态分解(VMD)和局部保持投影(LPP)相融合的轴承故障特征频率提取方法.该方法主要有三个步骤:一是利用VMD对信号进行分解,得到若干个本征模态分量(IMF),并将各分量组成高维信号矩阵;二是利用LPP对高维信号矩阵进行降维得到低维信号矩阵,而后进行信号重构,得到重构信号;三是对重构信号进行包络分析,根据包络谱中突出的频率成分判断轴承故障类型.轴承故障诊断实例验证了方法的有效性.     

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.     

柴油机振动信号快速稀疏分解与二维特征编码

针对柴油机故障诊断方法中的信号时频表征及特征提取问题,提出一种基于振动信号快速稀疏分解与二维时频特征编码识别的柴油机智能故障诊断方法。首先,为了获得时、频聚集性优良的时频图像,提出一种随分解残差信号自适应更新Gabor字典的改进匹配追踪(adaptive matching pursuit,简称AMP)算法,利用AMP算法将柴油机振动信号分解后叠加各原子分量的Wigner-Ville分布,获取原信号的稀疏分解时频图像;然后,为提取时频图像的特征参量,提出了双向二维非负矩阵分解(two-directional,2-dimensional non-negative matrix factorization,简称TD2DNMF)算法,用于对时频图像的幅值矩阵进行特征编码,获取蕴含在时频图像内部的低维特征,并利用最近邻分类器实现了时频图像的自动分类识别。将提出的方法应用于4种不同状态柴油机气门故障的诊断试验中,结果表明,该方法能够获得无交叉项干扰、聚集性好的时频图像,使各时频分量的物理意义更加明确,并改进了传统图像模式识别中的特征参数提取方法,是一种有效的柴油机故障诊断方法。     

单时频谱非负矩阵编码与解调的特征提取

非负矩阵分解(NMF)作为一种矩阵分解以及非线性维数约简工具,被广泛用于多样本振动时频谱的分解编码以及特征提取,但单样本振动时频谱的NMF编码、尤其NMF分解向量与振动时频谱分量间关联关系尚缺乏探讨。阐述了单时频谱编码与解调的特征提取原理,重点分析了NMF对单时频谱基于部分的特征表示能力、单时频谱NMF基向量的带通滤波幅频特性(BFAC)、以及NMF编码向量与时频谱分量的同步变化特性。提出单时频谱NMF编码与解调的两种特征提取新方法,即基于NMF基向量的滤波解调和NMF编码向量直接解调,定义一种BFAC指数指标和基向量归一化的NMF编码优化迭代规则分别用于NMF低维参数自适应选取和优化求解过程。将所提方法用于仿真信号以及齿轮箱振动信号分析,6.4 k长度数据在给定因子分解秩和NMF最大迭代300次终止条件设定下的特征提取用时约3.5 s,同时实现了对信噪比为-10 dB仿真信号以及多故障齿轮箱振动信号中故障特征的提取。     

Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet

The presence of periodical impulses in vibration signals usually indicates the occurrence of rolling element bearing faults. Unfortunately, detecting the impulses of incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher-level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We proposed a differential evolution (DE) optimization and antisymmetric real Laplace wavelet (ARLW) filter-based method to extract the impulsive features buried in noisy vibration signals. The wavelet used in paper is developed from the fault characteristic signal model based on the idea of sparse representation in time-frequency domain. We first filter the original vibration signal using DE-optimized ARLW filter to eliminate the interferential vibrations and suppress random noise, then, demodulate the filtered signal and calculate its envelope spectrum. The analysis results of the simulation signals and real fault bearing vibration signals showed that the proposed method can effectively extract weak fault features.     

改进时频分析和特征融合在内燃机故障诊断中的应用

针对基于内燃机振动信号的故障识别诊断问题,首先提出一种基于阈值筛选的变分模态分解(VMD)、玛基诺-希尔时频分布(MHD)的时频分析方法,该方法针对Cohen类时频分布存在的交叉干扰项问题,通过阈值筛选法确定VMD算法的分解层数,从而将内燃机振动信号分解成一系列单分量模态信号,然后对单分量信号进行MHD时频表征及线性叠加得到时频聚集性优良、物理意义明确的振动信号时频谱图。再通过局部非负矩阵分解(LNMF)对时频图像特征进行提取,将提取的特征与振动信号时域参数进行特征融合,得到融合特征向量。对支持向量机(SVM)采用改进粒子群优化算法进行参数优选,然后对特征向量进行训练和测试,实现了内燃机的故障识别诊断。将该方法应用于内燃机气门间隙故障8种工况下缸盖振动信号的识别诊断试验,结果表明,该方法能够对不同工况振动信号进行有效识别分类。通过参数优选,最高识别率达到了99.17%,同时对比传统的最近邻分类器的分类结果,证明了该方法的优越性。     

Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.     

Multiscale singular value manifold for rotating machinery fault diagnosis

Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.     

Diagnostics and prognostics based on adaptive time-frequency feature discrimination

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/ vibration measurements. The capability is based on extracting relevant features of noise/ vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.     

基于时频图像极坐标增强的柴油机故障诊断

针对柴油机不同部位的机械故障特征容易混淆且呈现非平稳循环特征的特点,提出了一种基于时频图像极坐标增强的柴油机故障诊断方法。将振动信号Gabor变换的时频特征通过等角度采样映射为极坐标图上某一区域的显著增强的特征,实现了周期瞬态特征的增强。提取不同技术状态振动信号6个工作循环内的极坐标图上区域能量特征作为故障特征参数,输入支持向量机进行分类训练和模式识别。试验结果表明,针对柴油机的5种典型故障,该方法能显著增强故障特征,有效提取故障特征信息,准确识别出不同类型的磨损故障。     

高速列车转向架区气动噪声分离研究

提出了一种基于快速路径优化的自适应短时傅里叶变换时频分析方法,并将该方法用于行星齿轮箱的故障诊断。该时频分析方法通过使用快速路径优化获得瞬时频率变化规律,在短时傅里叶变换过程中自适应的改变时窗长度,从而获得更恰当的时频分辨率。针对行星齿轮箱运行状态不稳定的特点,通过使用笔者提出的时频分析方法可以有效地提取出行星齿轮箱的转速信息,利用参考转速对故障信号角度域重采样和阶次分析,从而实现变转速情况下的行星齿轮箱故障诊断。仿真分析表明,与传统短时傅里叶变换相比基于快速路径优化的自适应短时傅里叶变换得到的时频分布能量更加集中;试验分析证明了基于快速路径优化的自适应短时傅里叶变换方法在行星齿轮箱故障诊断中的有效性。