【摘要】 在信号处理中,现有的常规指标如峭度、峰值、裕度以及谱峭度等对信号因偶然因素引起的数据奇异通常十分敏感,在轴承的状态监测中容易引起误判断。针对这一问题,提出了基于时频的频带熵方法。对信号进行时频变换,再沿时间轴计算各个频率上的幅值谱熵,得到信号的频带熵,以此为特征进行轴承故障的识别。频带熵表征频率成分随时间变化的的复杂性。正常与故障状态的轴承信号频率成分变化的复杂性不同,其频带熵也就不同,因此可将频带熵用于轴承故障的识别。同时偶然因素引起的数据奇异对频率成分变化的复杂性影响很小,频带熵可自动消解这些因素的影响,从而减少对轴承状态的误判断。将频带熵方法用于实际滚动轴承故障的识别,并与峭度、峰值、谱峭度指标对比,证明频带熵能够有效排除数据奇异的干扰,准确判别轴承状态,具有实用性。更多还原

【Abstract】 In signal processing,existing conventional indicators,such as,kurtosis,peak,margin,spectral kurtosis and other factors are usually very sensitive to accidental singularity of signal data,it is easy to lead to false judgments in bearing condition monitoring.To solve this problem,a spectral band entropy(SBE) method based on time-frequency was proposed.Firstly,a time-frequency transformation was made to a signal,and then the spectral entropy for each frequency along time axis was calculated to form the SBE of the signal,and it was used as a feature for bearing fault identification.SBE characterized the complexity of frequency components change with time.This complexity was different for a bearing’s normal state and its fault state,and the behavior of SBE was also different,so it could be used for bearing fault identification.At the same time,the data singularity caused by casual factors had little effect on the complexity of the frequency components change,SBE could automatically clear up these effects to reduce false judgments.The results of SBE real applications showed that compared with kurtosis,peak,spectral kurtosis and other indicators,SBE can effectively exclude interference from data,and identify bearing states accurately.更多还原