Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, People's Republic of China.
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Yunnan, Kunming 650500, People's Republic of China.
Rev Sci Instrum. 2023 Jan 1;94(1):015110. doi: 10.1063/5.0121593.
When the load and speed of rotating machinery change, the vibration signal of rolling bearing presents an obvious nonstationary characteristic. Stochastic resonance (SR) mainly is convenient to analyze the stationary feature of vibration signals with high signal-to-noise ratio. However, it is difficult for SR to extract the nonstationary feature of rolling bearings under strong noise background. For one thing, the frequency change of nonstationary signals makes the occurrence of SR very difficult. For another, the features of rolling bearings are large parameters and further prevent the SR method from performing well. Therefore, combined with order analysis (OA), adaptive frequency-shift SR is presented in this paper. To solve the problem of frequency change, OA is used to convert the nonstationary feature into stationary feature, which resamples the nonstationary signal in the time domain to stationary signal in the angular domain. To solve the other problem, the frequency-shift method based on Fourier transform is adopted to move the fault feature frequency to low frequency, and thus SR is more likely to occur under small parameter conditions. The simulated and experimental results indicate that not only the amplitude of fault feature but also the signal-to-noise ratio is significantly improved. These demonstrate that the fault features of rolling bearing in variable speed conditions are extracted successfully.
当旋转机械的负载和速度发生变化时，滚动轴承的振动信号呈现出明显的非平稳特征。随机共振（SR）主要用于分析具有高信噪比的振动信号的平稳特征。然而，SR 很难提取强噪声背景下滚动轴承的非平稳特征。一方面，非平稳信号的频率变化使得 SR 的发生变得非常困难。另一方面，滚动轴承的特征是大参数，这进一步阻止了 SR 方法的良好表现。因此，本文结合阶次分析（OA），提出了自适应频移 SR。为了解决频率变化的问题，OA 用于将非平稳特征转换为平稳特征，即在时域中将非平稳信号重采样为角域中的平稳信号。为了解决另一个问题，采用基于傅里叶变换的频移方法将故障特征频率移动到低频，从而在小参数条件下更有可能发生 SR。仿真和实验结果表明，不仅故障特征的幅度，而且信噪比都得到了显著提高。这些表明成功提取了变速条件下滚动轴承的故障特征。
