School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
ISA Trans. 2023 Jul;138:611-627. doi: 10.1016/j.isatra.2023.02.017. Epub 2023 Feb 14.
A key problem in the fault diagnosis of rolling element bearings is the extraction of features of repetitive transients from vibration signals. The accurate evaluation of maximizing spectral sparsity under complex interference conditions for measuring the periodicity of transients is typically difficult to implement. Accordingly, a novel periodicity measurement approach was designed for time waveforms. According to the Robin Hood criteria, the Gini index of a sinusoidal signal has a stable low sparsity. The periodic modulation of cyclo-stationary impulses can be represented by several sinusoidal harmonics based on envelope autocorrelation and bandpass filtering. Thus, this low sparsity of Gini index can be used to evaluate the periodic strength of modulation components. Finally, a sequential feature evaluation method is developed to extract periodic impulses accurately. The proposed method is tested on simulation and bearing fault datasets and compared with the state-of-art methods so to assess its effectiveness.
滚动轴承故障诊断中的一个关键问题是从振动信号中提取重复瞬变的特征。在复杂干扰条件下准确评估最大化谱稀疏度以测量瞬变的周期性通常难以实现。因此，针对时间波形设计了一种新颖的周期性测量方法。根据罗宾汉准则，正弦信号的基尼指数具有稳定的低稀疏性。基于包络自相关和带通滤波，循环平稳冲击的周期性调制可以用几个正弦谐波来表示。因此，基尼指数的这种低稀疏性可用于评估调制分量的周期性强度。最后，开发了一种顺序特征评估方法来准确提取周期性脉冲。所提出的方法在模拟和轴承故障数据集上进行了测试，并与最先进的方法进行了比较，以评估其有效性。
