基于局部特征尺度分解与瞬时频率谱的齿轮故障诊断

为了更准确地进行齿轮故障诊断,根据齿轮故障振动信号的多分量调幅-调频特征,提出了局部特征尺度分解和瞬时频率谱相结合的故障诊断方法。该方法首先对齿轮振动信号运用局部特征尺度分解,得到若干个内禀尺度分量,然后分别应用希尔伯特变换技术求取每个分量的瞬时频率,最后根据瞬时频率谱并进行故障诊断。通过齿轮断齿故障试验数据分析,验证了方法的有效性。     

基于BLCD分解与ACO-DBN网络的光纤振动信号识别

针对光纤振动信号受噪声干扰严重、特征提取单一和识别时间长的问题,提出了改进的局部特征尺度分解和蚁群算法优化深度置信网络的识别方法。首先,采用三次B样条函数插值拟合均值曲线改进局部特征尺度分解算法,并对原始信号进行分解得到一系列内禀尺度分量之和。其次,利用峭度因子和能谱系数构成融合指标筛选有效分量。然后,分别提取有效分量在时域、频域和时-频域的熵值特征进行融合并降维。最后,将综合特征向量馈入蚁群优化后的深度置信网络进行训练和识别,提高算法效率和识别率。采用实测数据进行实验验证,结果表明,信噪比平均提升8 dB,信号平均识别率可达95.83%,平均识别时间为0.715 s。     

超声辅助加工系统的刀具状态自感知算法

超声波振动台内含压电材料,可以拾取切削过程产生的振动信号,实现不借助外部传感器刀具工作状态的自感知。为了从刀具振动信号中获取有效信息,该文提出一种基于经验模态分解的时频域重构算法。首先,采用经验模态分解算法将原始信号分解,得到多个固有模态函数分量和残差分量;其次,计算原始信号与各分量之间的时频域互相关系数;再次,归一化时频域互相关系数作为权重值,将固有模态函数分量和残差进行重构;最后,通过数值仿真和超声辅助加工实验,验证了基于经验模态分解的时频域重构算法的去噪性能,提取了信噪比为5.03 dB的目标信号,从而实现了超声辅助加工系统的自感知功能。     

基于EMD的谱峭度方法在滚动轴承故障检测中的应用

传统谱峭度方法通常采用基于短时傅里叶变换(Short Time Fourier Transform,STFT)的峭度图方法来实现。针对STFT不能保证对瞬态脉冲这种高度非平稳信号最优的分解效果的缺点,提出一种基于经验模式分解(Empirical Mode Decomposition ,EMD)的谱峭度方法。该方法首先利用EMD和Hilbert变换得到信号的时频分布,然后将信号的时频分布按照不同层数分成若干频段,通过计算各频段的峭度值得到相应的峭度图,再根据峭度最大原则选择滤波频段进行带通滤波,最后对滤波信号采用包络分析确定故障信息。实验结果表明：相比传统基于STFT的谱峭度方法,本文方法更能准确的获得轴承加速度信号的故障特征频率信息。     

航空物流传送设备中轴承故障稀疏特征提取

航空物流传送设备中的轴承由于长期受外侵灰尘影响,其内外环极易发生故障;利用计算机采集轴承的振动信号并进行故障特征提取是轴承故障诊断的常用方法;提出了基于稀疏分解的轴承故障特征提取方法;首先,分析轴承故障特征稀疏提取原理;然后,构造参数化Gabor字典,利用遗传算法对故障特征成分进行匹配追踪 (Matching Pursuit,简称MP),以峭度值最大原则作为迭代结束条件;最后,重构提取的特征成分,进行包络谱分析,得出故障类型;对仿真数据和轴承振动数据的测试结果表明,所提方法能有效提取轴承故障特征成分,为航空物流传送设备中轴承的故障监测提供了一种有效方法。     

基于IFD与DE-ELM的轴承故障诊断

针对局部均值分解（LMD）实现过程中存在的模态混淆现象和端点效应,影响识别精度的问题,提出了一种基于本征频率尺度分解（IFD）与差分进化极限学习机（DE-ELM）的方法。该方法将数字图像处理的频率分辨率概念与LMD结合起来;首先确定原始振动信号中的所有局部极值点的频率分辨率,将振动信号分为高频率分辨率区域和低频率分辨率区域;然后,构造本征频率尺度函数,将本征频率尺度函数添加到局部极值点低频率分辨率区域;最后,对添加本征频率尺度函数的原始振动信号进行LMD分解,在得到的乘积函数（PF）分量中剔除本征频率尺度函数,就可以得到突出原始信号振动特征的不同频率分辨率的PF分量,提取PF分量的特征参数构建特征向量作为DE-ELM的输入,进行故障类型识别。将该方法应用于轴承故障诊断,与LMD相比,故障识别精度提高了8.33%,表明了该方法的有效性与可行性。     

基于经验模态分析的故障诊断方法及应用研究

为实现对微弱动态响应的准确辨识及故障状态的早期诊断,提出了基于经验模态分析的故障诊断方法,将模态分解、互信息熵与主元分析结合,故障特征更凸显,方法更有效。首先模态分解,得到一系列固有模态分量,利用互信息熵判断所有固有模态分量的高低频分界点并对高频分量自适应阈值去噪。将去噪后的所有高频分量和低频分量主元分析,计算各主元的峭度值,选取峭度值大的分量求时频谱得故障频率,从而确定故障。将该方法应用到含有高频环境噪声的轴承故障信号中诊断可靠、准确。     

基于自适应多尺度时频熵的遥测振动信号异常检测方法

针对遥测振动信号频域成份复杂、非平稳非线性和强噪声特性,提出一种基于自适应多尺度时频熵的遥测振动信号异常检测方法;首先对采集到的遥测振动信号进行零漂修正和趋势项消除,然后采用自适应分解方法对信号进行多尺度分解,得到若干分量,利用相关系数剔除虚假分量;接下来用筛选出的分量作时频分布,对时频分布进行多层多尺度划分,计算相应尺度频段内信号的分形维数,依据分形维数的大小自适应地确定各频段的时频划分尺度;最后计算时频平面的自适应多尺度时频熵,通过时频熵的变化情况对遥测振动信号进行异常检测;实测数据验证了该方法的有效性。     

基于DE-LSSVM的滚动轴承故障诊断

针对滚动轴承故障诊断难以获得大量样本的问题以及LS-SVM 模型参数选择方法易陷入局部最优的缺点,提出了一种集合经验模态分解能量熵和差分进化算法（DE）优化最小二乘支持向量机相结合的轴承故障诊断方法。首先原始振动信号采用EEMD分解得到一组固有模态函数（IMF）,从有效本征模态函数IMF分量中提取的能量特征作为输入建立支持向量机,通过计算不同振动信号的能量熵值大小来判断轴承的故障损伤程度。为了提高模型的诊断精度,采用差分进化算法对LS-SVM的结构参数进行优化,并与LS-SVM和PSO-LSSVM模型相比较。结果表明,DE-LSSVM 模型的故障分类准确性得到了提高,可以有效应用于滚动轴承故障诊断中。     

基于小波变换的人体步态序列提取

将小波变换用于处理人体行走时产生的加速度信号.利用离散小波变换的多尺度、多分辨率特性对原始加速度信号进行尺度分解,在对小波基以及分解尺度进行合理选取后准确地从加速度信号中提取出隐藏的步态节律.与利用阈值法直接对原始加速度信号提取峰值的算法比较后发现：利用小波分解得到与步态节律相关的特征尺度后再进行峰值检测能显著地提高信号峰值的检出率;即使当原始信号存在较严重的噪声干扰时,该方法也能保证所提取出的步态序列的准确性.这对于步态序列的后续分析具有至关重要的意义.研究表明,离散小波变换是一种有效的提取步态节律的方 关键词： 小波变换 步态序列 峰值检测 特征尺度     

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.     

Application of correlation matching for automatic bearing fault diagnosis

This paper introduces an automatic feature extraction algorithm for bearing fault diagnosis using correlation filtering-based matching pursuit. This algorithm is described and investigated in theory and practice on both simulated and real bearing vibration signals. First, the vibration model for rolling bearing with fault is derived. Then, the numerical simulation signal being taken as an example, the principle of matching pursuit is mathematically explained and its drawbacks are analyzed. Afterward, to enhance the similarity of model related to the bearing faulty impulses, the model shape parameters are optimized using spectrum kurtosis and smoothing index. After that, the model with optimum shape and period parameters is taken as a template to approximate the impulses in faulty bearing signal. Finally, based on maximizing correlation principle, the optimized cycle parameter being as impuls e repetition period is matched up. The proposed method has been successfully applied in actual vibration signals of rolling element bearing with different faults.     

A new blind fault component separation algorithm for a single-channel mechanical signal mixture

A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two parts: the periodic and transient subsets. The periodic subset is related to the imbalance, misalignment and eccentricity of a machine. The transient subset refers to abnormal impulsive phenomena, such as those caused by localized bearing faults. The proposed method includes two individual strategies to deal with these different characteristics. The first extracts the sub-Gaussian periodic signal by minimizing the kurtosis of the equalized signals. The second detects the super-Gaussian transient signal by minimizing the smoothness index of the equalized signals. Here, the equalized signals are derived by an eigenvector algorithm that is a successful solution to the blind equalization problem. To reduce the computing time needed to select the equalizer length, a simple optimization method is introduced to minimize the kurtosis and smoothness index, respectively. Finally, simulated multiple-fault signals and a real multiple-fault signal collected from an industrial machine are used to validate the proposed method. The results show that the proposed method is able to effectively decompose the multiple-fault vibration mixture into periodic components and random non-stationary transient components. In addition, the equalizer length can be intelligently determined using the proposed method.     

基于相关性原理起重机负载电机监测系统设计

对起重机负载电机进行了研究,采用西门子公司的S7-200 SMART PLC采集负载电机的机械振动信号,通过工业Wi-Fi无线模块以无线数据包的形式将采集的数据汇总到上位机LabVIEW监测平台。上位机的LabVIEW监测平台对电动机振动信号进行相关性和频谱分析,将实时振动数据频谱信号和已知常见负载电机的轴承外圈故障、轴承内环故障和滚子故障三种典型的故障状态频谱信号进行相关性运算,得到实时信号与已知状态的相关系数;提出了以相关系数作为故障诊断判定阈值的方法进行故障诊断,实现了对起重机状态进行监测以及监控信息发布。     

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.     

Envelope order tracking for fault detection in rolling element bearings

An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.     

Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis

Integer-scale structuring element is usually used in the traditional mathematical morphology (MM) for signal processing. When applied for impulsive feature demodulation of vibration signal of rolling element bearings, the integer-scale MM (ISMM) may lead to low resolution result and thus undermines its defect diagnosis capability. For this reason, this paper proposes a continuous-scale MM (CSMM) scheme by interpolation and re-sampling to improve scale resolution for more reliable fault signature extraction. Based on the frequency domain kurtosis criterion, a narrowband merging operation is employed to locate the optimal scale band that best reflects the impulsive feature from the CSMM analysis results. The demodulated components in the optimal scale band are employed to detect the existence of the bearing fault. The proposed optimal CSMM demodulation technique is evaluated using both simulated and experimental bearing vibration signals. The results show that, the CSMM is capable of generating demodulation signals with higher resolution, and the optimal scale band demodulation based on the CSMM can reliably extract impulsive features for bearing defect diagnosis.     

Identification of multiple transient faults based on the adaptive spectral kurtosis method

This paper proposes a multi-fault detection method based on the adaptive spectral kurtosis (ASK) analysis of the vibration signal from single sensor. A theoretical model of multiple bearing faults is established in this paper. Compared with the kurtogram and protrugram techniques, the proposed method can more effectively extract signatures of multiple bearing faults even in the presence of strong background noise. The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。