可调品质因子小波变换在滚动轴承微弱故障特征提取中的应用

Application of Tunable Q-factor Wavelet Transform to Feature Extraction of Weak Fault for Rolling Bearing

针对滚动轴承早期微弱故障识别困难的问题,提出一种基于可调品质因子小波变换的特征提取方法。利用可调品质因子小波对原始信号进行处理,将其分解为若干子带信号,为了使所得信号分量包含尽可能多的故障相关信息,以峭度为指导标准对子带信号做进一步合并处理,并从所得结果中筛选出峭度极值分量,利用相关系数准则剔除冗余分量后,通过分析保留信号分量的包络谱来判断轴承的故障类型。利用所述方法对仿真信号和试验信号进行分析,均成功提取出微弱特征频率成分,由此表明该方法可实现轴承早期故障的精确诊断,具有一定可靠性和应用价值。

The weak fault feature of rolling bearing in early failure period is difficult to identify. A method of feature extraction based on tunable Q-factor wavelet transform was proposed. The original signal was processed by tunable Q-factor wavelet and was decomposed into several sub-band signals, in order to acquire signal component which contains fault related information as much as possible, merging operation which was guided by kurtosis criterion was performed on sub-band signals and the kurtosis extreme components were selected from the obtained results. Correlation coefficient criterion was used further to eliminate the redundant components and the fault type of bearing could be judged by analyzing the envelope spectrum of the remained signal components. Both simulated and experiment signals were analyzed by proposed method and the weak characteristic frequency components were extracted successfully, the results showed the proposed method could diagnose the incipient fault of bearing precisely and had a certain reliability and application value.