An advanced Park's vectors approach for bearing fault detection

Induction motors vibrations, caused by bearing defects, result in the modulation of the stator current. In this research, a method based on Park's vector approach for bearing fault detection using three-phase stator current analysis is presented. In order to evaluate the ability of the proposed method several experiments are performed, and sets of data are gathered before and after using defective bearings. Both localized and distributed defects are evaluated using this method. The experimental results from our study suggest that the proposed method provides a powerful and general approach to incipient fault detection.     

基于EEMD样本熵的电机轴承电流信号复杂性评估

电机轴承损伤会导致电机定子电流产生相应的电流谐波,电流谐波频率包含轴承故障特征频率。为了有效评估定子电流信号的复杂性（即电流谐波出现概率）,采用总体平均经验模态分解（EEMD）结合样本熵来实现。该方法先用EEMD将定子电流信号分解为若干个内禀模态分量,再计算分量的样本熵。通过比较得出在评估损伤轴承定子电流信号复杂性时EEMD样本熵的效果较样本熵更好,并且EEMD样本熵增大一减小一增大的变化趋势与轴承损伤逐渐加大时定子电流的变化趋势一致。根据上述结论该方法可应用于封闭结构中电机轴承运行状态的监测和预判,也可以作为智能故障识别的信号源。     

基于Park矢量的电动机轴承故障检测方法

提出了一种基于Park矢量的改进聚类处理算法,该方法通过辨识感应电动机三相定子电流中的故障信息来识别轴承故障。为了验证该方法的有效性,在电动机轴承上预设了故障,通过数字信号处理器采集数据,并利用上述方法进行处理。结果表明,所提出的方法可有效识别电动机的轴承故障。     

基于高分辨率谱估计的早期转子断条故障诊断

以快速傅里叶变换(FFT)为基础的电机电流信号特征分析(MCSA)具有频率分辨率低的固有缺陷,从而严重影响了鼠笼电机早期转子断条故障的诊断性能。为解决这一问题,提出基于高分辨率谱估计的早期转子断条故障诊断方法。首先利用Hilbert变换和离散小波变换对单相定子电流信号预处理,然后采用扩展Prony算法对预处理后的信号进行定性/定量分析。运用该方法对不同故障严重程度、不同负载条件下的3 k W电机稳态定子电流信号进行分析,并与FFT分析结果做对比。实验结果表明,即使在短时数据条件下所提方法仍然能够准确诊断出早期转子断条故障,验证了该方法的有效性和优越性。     

Wheel-bearing fault diagnosis of trains using empirical wavelet transform

Rolling bearings are used widely as wheel bearing in trains. Fault detection of the wheel-bearing is of great significance to maintain the safety and comfort of train. Vibration signal analysis is the most popular technique that is used for rolling element bearing monitoring, however, the application of vibration signal analysis for wheel bearings is quite limited in practice. In this paper, a novel method called empirical wavelet transform (EWT) is used for the vibration signal analysis and fault diagnosis of wheel-bearing. The EWT method combines the classic wavelet with the empirical mode decomposition, which is suitable for the non-stationary vibration signals. The effectiveness of the method is validated using both simulated signals and the real wheel-bearing vibration signals. The results show that the EWT provides a good performance in the detection of outer race fault, roller fault, and the compound fault of outer race and roller.     

EMD分解在电动机轴承故障诊断中的应用

选取单相功率频谱作为分析对象,通过对单相功率频谱进行EMD分解,准确地提取了fv故障特征量。仿真结果表明,该方法诊断灵敏度高、直观清晰,有效解决了定子电流中故障分量被基波分量淹没的难题,是一种有效的可行方法。     

Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms

As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes an improved combination of the Hilbert and wavelet transforms to identify early bearing fault signatures. Real rail vehicle bearing and motor bearing data were used to validate the proposed method. A traditional combination of Hilbert and wavelet transforms was employed for comparison purpose. An indicator to evaluate fault detection capability of methods was developed in this research. Analysis results showed that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method.     

基于PPCA-EWT的滚动轴承轻微故障诊断

针对经验小波变换(empirical wavelet transform,简称EWT)在强背景噪声下对轴承的轻微故障特征提取不足的问题,提出了概率主成分分析(probabilistic principal component analysis,简称PPCA)结合EWT的滚动轴承轻微故障诊断方法。首先,对信号做PPCA预处理,提取信号主要故障特征成分,去除强背景噪声干扰;然后,采用EWT方法分解轴承故障信号,按相关系数-峭度准则选出故障特征较为明显的分量,并将所选分量重构故障信号;最后,对信号采取包络分析,提取出轴承故障特征。仿真和实验结果表明,该方法能够有效地诊断出轴承故障且效果优于对信号进行EWT包络分析。     

基于多尺度Hermitian小波包络谱的轴承故障诊断

提出了一种基于多尺度Hermitian小波包络谱的轴承故障诊断方法。该方法综合利用了Hermitian小波和包络谱分析技术的优点,首先对轴承故障振动信号进行Hermitian连续小波变换,得到小波分解的实部和虚部,然后计算振动信号的多尺度包络谱。对齿轮箱轴承故障振动信号的分析表明,该方法在强噪声环境下能有效识别轴承内圈故障和外圈故障。     

基于连续小波变换的信号检测技术与故障诊断

通过分析指出,连续小波变换具有很强的弱信号检测能力,非常适合故障诊断领域。从参数离散到参数优化系统研究了连续小波变换的工程应用方法,建立了“小波熵”的概念,并以此作为基小波参数的择优标准。论文最后把连续小波技术应用在滚动轴承滚道缺陷和齿轮裂纹的识别中,诊断效果十分理想。