A weighted multi-scale morphological gradient filter for rolling element bearing fault detection

This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states.     

基于包络谱分析的滚动轴承故障诊断分析

介绍了包络谱分析方法的基本原理,它是一种基于滤波检波的振动信号处理方法,也是诊断设备零件损伤故障的一种有效的手段,尤其对初期故障和信噪比比较第的故障信号,识别能力很强。重点分析了包络谱分析方法在轴承故障诊断中的应用。通过对滚动轴承故障诊断的实例分析,验证了包络谱分析运用于诊断设备零件损伤故障所取得的效果。     

Application of EEMD and improved frequency band entropy in bearing fault feature extraction

Ensemble empirical mode decomposition (EEMD) is widely used in condition monitoring of modern machine for its unique advantages. However, when the signal-to-noise ratio is low, the de-noising function of it is often not ideal. Thus, a new fault feature extraction method for rolling bearing combining EEMD and improved frequency band entropy (IFBE) is proposed, i.e., EEMD–IFBE. According to the problem of multiple intrinsic mode functions (IMFs) generated by EEMD, how to select the sensitive IMF(s) that can better reflect fault characteristics, a novel method based on FBE for sensitive IMF is proposed. In addition, since the bandwidth parameter is set empirically when the band-pass filter is designed based on the original FBE, a novel bandwidth parameter optimization method based on the principle of maximum envelope kurtosis is proposed. First, the original vibration signal is subjected to EEMD to obtain a series of IMFs; Then, the FBE values are obtained for the original signal and each IMF component, and the bandwidth of the band-pass filter (empirically) is designed as the characteristic frequency band at the minimum entropy value, and the affiliation between the characteristic frequency band of each IMF and the characteristic frequency band of the original signal is compared, and then selecting the sensitive IMF(s) that reflects the characteristics of the fault; Third, due to the influence of background noise, it is difficult to accurately obtain the fault frequency from the selected IMF(s). Therefore, the band-pass filter designed based on FBE is used, and the bandwidth parameter is optimized based on the principle of envelope kurtosis maximum, and then the selected sensitive IMF is band-pass filtered. Finally, the envelope power spectrum analysis is performed on the filtered signal to extract the fault characteristic frequency, and then the fault diagnosis of the bearing is realized. The method is successfully applied to simulated data and actual data of rolling bearing, which can accurately diagnose fault characteristics of bearing and prove the effectiveness and advantages of the method.     

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。     

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。     

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

Targeting the modulation characteristics of roller bearing fault vibration signals, a method of fault feature extraction based on intrinsic mode function (IMF) envelope spectrum is proposed to overcome the limitations of conventional envelope analysis method. By utilizing the proposed feature extraction method, the disadvantages of conventional envelope analysis method such as the chosen of central frequency of filter with experience in advance, looking for spectral line of fault characteristic frequencies in envelope spectrum and so on could be overcome. Firstly, the original modulation signals are decomposed into a number of IMFs by empirical mode decomposition (EMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some IMFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios serve as the fault characteristic vectors to be input to the support vector machine (SVM) classifiers and the work condition and fault patterns of the roller bearings are identified. Since the recognition results are available directly from the output of the SVM classifiers, the proposed diagnosis method provides the possibility to fulfill the automatic recognition to machinery faults.     

Bearing fault diagnosis based on an improved morphological filter

The extraction of repetitive impacts from vibration signals plays an essential role in bearing fault detection. Among different signal processing algorithms, morphological filter (MF) has attracted lots of attention because it could directly extract the geometric structure of the impulsive feature and only needs little computation. However, the conventional MF and some current improvements are based on the local optima of the raw signal to de-noise the noisy signal and its faulty feature extracting capability would be greatly affected by the noise. In this paper, a new improved MF algorithm is proposed to overcome such deficiency. Firstly, morphological gradient (MG) operator is selected in this paper due to its capability of picking up both positive and negative impulses. Then, based on the relationship between the defect induced impulse and a harmonic function with the resonant frequency, the harmonic waveform in a period is adopted to instruct the construction of structuring element (SE). The improved MF can obtain the fault feature from low SNR signals. The processing results of a simulation signal and two sets of experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method.     

Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM

As it is difficult to identify the scale and aperture of small leaks occurring in a natural gas pipeline, this paper proposes a small leak feature extraction and recognition method based on local mean decomposition (LMD) envelope spectrum entropy and support vector machine (SVM). First, LMD is used to decompose the leakage signals into several FM–AM signals, i.e. into product function (PF) components. Then, based on their kurtosis features, the principal PF components that contain most of the leakage information are selected. Wavelet packet decomposition and energy methods are used to analyze and then reconstruct the principal PF components. The Hilbert transform is applied to these reconstructed principal PF components in order to acquire the envelope spectrum, from which the envelope spectrum entropy is obtained. Finally the normalized envelope spectrum entropy features are input into the SVM as leakage feature vectors in order to enable leak aperture category identification. By analyzing the acquired pipeline leakage signals in field experiments, it shows that this method can effectively identify different leak categories.     

滚动轴承数据库设计及其在故障诊断中的应用

针对国内外滚动轴承种类繁多、编号复杂以及轴承故障特征频率难以获得的现状,利用Power Builder强大的数据库功能,设计出一套数据完整、查询快捷方便,并与瑞典SKF公司的轴承故障特征频率参数相吻合的数据库系统,同时举例说明该系统可广泛应用于设备状态监测、故障诊断和预知维修领域。     

基于全矢小波包能量熵的滚动轴承智能诊断

研究滚动轴承不同状态下的振动信号,使用小波包变换提取信号各频带的能量熵,作为轴承故障的特征,然后使用支持向量机智能诊断轴承不同故障。传统单通道信号诊断方法容易造成误诊,全矢小波包能量熵融合了振动信号双通道的信息,能更准确地反映故障的特征。实验结果表明,采用全矢小波包能量熵比传统单通道方法有更高的诊断精度。