1.石家庄铁道大学 省部共建交通工程结构力学行为与系统安全国家重点实验室，石家庄，050043;2.石家庄铁道大学 机械工程学院，石家庄，050043
1.State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043,China;2.School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
A novel adaptive empirical wavelet tower decomposition method was proposed to solve the problem that the weak fault features of gears are difficult to be diagnosed under strong background noise. Firstly, the Fourier transform was utilized to process the vibration signals of gear faults. The frequency spectrum of a signal could be effectively segmented according to the number of decomposition layers, and the empirical wavelet transform (EWT) was applied to extract the intrinsic modes of the signal. Secondly, the time-frequency kurtosis index was proposed to evaluate the performance of mode signals. Then the time-frequency kurtosis diagram of all mode signals under different decomposition layers was acquired to determine the frequency band range of the optimal mode signal. Finally, weak fault features of gears were extracted through envelope demodulation analysis for the optimal mode signal. Experimental results showed that the proposed method can effectively improve the weak fault detection of gears, and eliminate the errors caused by the strong background noise, which outperforms the traditional EWT method.
邓飞跃,丁浩,刘永强.自适应经验小波塔式分解的齿轮微弱故障诊断方法[J].动力学与控制学报,2020,18(3):100~106; Deng Feiyue, Ding Hao, Liu Yongqiang. A WEAK FAULT DIAGNOSIS METHOD OF GEAR WITH ADAPTIVE EMPIRICAL WAVELET TOWER DECOMPOSITION[J]. Journal of Dynamics and Control,2020,18(3):100-106.复制