强噪声下轴承故障频率的粒子群随机共振提取

Vari-scale stochastic resonance system based bearing fault frequency extraction under strong noise

为了提取强噪声背景下轴承故障的特征频率,设计了基于变尺度随机共振系统的有用信号增强方法。介绍了滚动轴承结构和不同位置故障的特征;分析了随机共振理论仅适用于低噪声、小频率的问题,针对性地设计了变尺度随机共振系统,扩展了该理论的应用范围;并提出了多行为粒子群算法的随机共振系统参数优化方法。经仿真验证,在信噪比为-20 dB的强噪声背景下,变尺度随机共振系统仍能够有效提取有用信号中的故障特征频率;经西安交通大学公开轴承实验数据集XJTU-SY验证,在强噪声背景下,采用稀疏重构法提取的故障特征频率仍被淹没在附近频域中,而变尺度随机共振系统提取的故障特征频率在159.7 Hz明显凸显,且提取的故障特征频率更接近真实值。实验结果表明,在强噪声背景下,变尺度随机共振系统能够有效提取振动信号中的故障特征频率。

In order to extract the bearing faults characteristic frequency under strong noise background,a vari-scale stochastic resonance system based useful signal enhancement method was designed.The structure and fault characteristics of bearing were introduced.The shortcoming of stochastic resonance theory to low noise and low frequency was analyzed,and a vari-scale stochastic resonance system was designed specifically,expanding the application scope of the theory.Then a multi-behavior particle swarm optimization algorithm for stochastic resonance system parameters optimization was proposed.Verified by simulation,under the strong noise background with a signal-to-noise ratio of-20 dB,the variable scale stochastic resonance system can still effectively extract feature frequencies from useful signals.According to the experimental data of bearings published by Xi′an Jiaotong University,under strong noise background,the fault frequency extracted by sparse reconstruction method is still submerged in the nearby frequency domain,while the feature frequency extracted by the variable scale stochastic resonance system is significantly prominent,and the feature frequency extracted by the method is closer to the true value.The experimental results indicate that the vari-scale stochastic resonance theory can effectively extract the characteristic frequencies in vibration signals under strong noise backgrounds.