CAJ | 학술논문

双线性时频分布能更全面地表征复杂背景下瞬态机械故障信号特征,但双线性时频变换固有的交叉项干扰严重影响了算法的时频分辨率。探讨了双线性时频分析技术在微弱瞬态信号辨识中的应用,提出采用ARMA模型滤波的方法来抑制双线性Wigner-Ville时频变换的交叉项干扰,并给出算法推导。结合实验数据,对比平滑伪Wigner-Ville算法的信号辨识结果,表明基于ARMA模型预滤波的双线性时频分析能更好的抑制交叉项干扰,具备更高的时频分辨能力和瞬态微弱信号辨识能力。
쌍선성시빈분포능경전면지표정복잡배경하순태궤계고장신호특정,단쌍선성시빈변환고유적교차항간우엄중영향료산법적시빈분변솔。탐토료쌍선성시빈분석기술재미약순태신호변식중적응용,제출채용ARMA모형려파적방법래억제쌍선성Wigner-Ville시빈변환적교차항간우,병급출산법추도。결합실험수거,대비평활위Wigner-Ville산법적신호변식결과,표명기우ARMA모형예려파적쌍선성시빈분석능경호적억제교차항간우,구비경고적시빈분변능력화순태미약신호변식능력。
Bilinear time-frequency distribution can token overall mechanical failure signal characteristics,but it＇s found that strong cross-terms exist which results in frequency aliasing and information loss.The time-frequency analyze is used to identificate the weak winking signal in complex background.Arithmetic based on ARMA model filter is bring forward to solve cross-terms problem.It is simulated in experiment data and contrasted to Smooth-Puppet Wigner-Ville arithmetic.The conclusion is that arithmetic of ARMA model pre-filter restrained cross-terms disturbance better and is of better weak winking signal identification ability.