关键词: 轴承故障, 特征频率, 随机共振系统, 变尺度, 多行为粒子群算法

Abstract: 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.

Key words: bearing fault, characteristic frequency, stochastic resonance system, vari-scale, multi-behavior particle swarm optimization algorithm