The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.
时频分布是机械滚动轴承故障信号的有效分析方法,特殊情况下的机械故障信号或噪声属于非高斯 Alpha(α)稳定分布,传统的 Stockwell 变换( S 变换)时频方法性能退化甚至失效。基于 S 变换时频和分数低阶矩提出了一种分数低阶 S 变换时频分布算法,为了减少计算量及在线及时分析信号,提出了一种快速分数低阶 S 变换时频算法。仿真结果表明,所提出的分数低阶 S 变换时频算法及其快速算法能很好地工作在高斯噪声和α稳定分布噪声环境,性能优于已有的 S 变换时频。在实际应用中,所提出的时频算法能够较好的提取机械轴承故障信号的故障特征。
