Order tracking demodulation analysis based on Ensemble Empirical Mode Decomposition and its applications to fault diagnosis

Abstract

Based on the feature of vibration signal of rotating machinery in speed fluctuation processes which are combined by many modulated signals and the fault characteristic frequency of it is varied following the rotating speed, and in order to suppress the phenomenon of mode mixing, a new method combined Order Tracking analysis with Ensemble Empirical Mode Decomposition(EEMD) algorithm were proposed. First of all, the time domain vibration signal were resample and transformed into angle domain stationary signal by using the Order Tracking technique,and then, the resample signal in angle domain is decomposed into several Intrinsic Mode Function components self-adaptively with EEMD. Finally, the envelope demodulation analysis is used to deal with high order components in angle domain and the fault features can be obtained. The analysis results from the simulation and experimental signals of out-race fault in rolling bearing fully proved the effectiveness of the proposed method.

Dynamic signal analysis for speed-varying machinery: A review

“…2011 年, 米兰理工大学 Ricci 和 Pennacchi [148] 提出了 IMF 的自适应选取方法, 能够 对螺旋锥齿轮有效诊断, 该方法对变转速情况同样 适用. 2011 年北京理工大学 Fang 等人 [149] 将阶次跟踪 解调法与集合经验模式分解(ensemble empirical mode decomposition, EEMD)结合, 提出了基于 EEMD 的阶 次跟踪解调分析方法, 对速度波动信号进行解调得 到故障特征, 并通过仿真与轴承故障数据验证了所 述方法的有效性. 2011 年, 南非比勒陀利亚大学的 Wang 和 Heyns [150] 对 COT, Vold-Kalman 滤波和 EMD 之间的联系进行深入分析和讨论, 文献将三者的优 势进行结合, 实现对回转机械振动信号的同步分量 和非同步分量进行有效识别和分离.…”