CAJ | 학술논문

因轴承的剥落、裂纹等局部故障易致运行时振动信号中出现瞬态成分，而轴承故障振动信号为非平稳信号，含高、低振荡成分，传统的线性信号处理方法及基于频率的分解方法均存在一定局限性。对此，研究基于信号振荡特征而非频率特征的双调 Q 小波变换，设定不同 Q 因子小波将轴承故障信号非线性分解成低、高振荡及噪声成分，轴承故障瞬态成分对应低振荡成分，提取低振荡成分即能实现轴承故障瞬态成分提取。通过轴承故障状态下瞬态成分检测表明，该方法能有效提取轴承故障瞬态成分。经与均值滤波、小波阈值及经验模态分解（EMD）等方法比较，验证该方法的优越性。
인축승적박락、렬문등국부고장역치운행시진동신호중출현순태성분，이축승고장진동신호위비평은신호，함고、저진탕성분，전통적선성신호처리방법급기우빈솔적분해방법균존재일정국한성。대차，연구기우신호진탕특정이비빈솔특정적쌍조 Q 소파변환，설정불동 Q 인자소파장축승고장신호비선성분해성저、고진탕급조성성분，축승고장순태성분대응저진탕성분，제취저진탕성분즉능실현축승고장순태성분제취。통과축승고장상태하순태성분검측표명，해방법능유효제취축승고장순태성분。경여균치려파、소파역치급경험모태분해（EMD）등방법비교，험증해방법적우월성。
Local faults in rotating machinery bearings are easy to cause transient impulse response components in vibration signals.In order to realize bearing fault diagnosis under strong noise conditions,it is crucial to extract fault features from vibration signals.But a bearing fault vibration signal is a non-stationary one,it consists of high and low resonance components,traditional linear methods and signal decomposition methods based on frequency have certain limitations.To overcome these limitations,a nonlinear signal analysis method named double tunable Q-factor wavelet transformation (double-TQWT)was proposed,it was based on signal resonance characteristics rather than frequency features.By using the double-TQWT,a bearing fault vibration signal was decomposed into high and low resonance components based on different resonance characteristics.The bearing fault transient component had a low Q-factor and was decomposed into low resonance components.Extracting these low resonance components could realize extracting bearing fault transient components.The transient components for bearing fault signals under strong noise conditions were extracted and analyzed.The results showed that the new method is superior to the average filtering method,the wavelet threshold algorithm,and the EMD.