基于DCT和EMD的滚动轴承故障诊断

根据轴承故障振动信号特点,提出了一种离散余弦变换和经验模态分解相结合的轴承振动信号故障诊断新方法。将离散时间序列经过离散余弦变换处理成对应的系数向量,在阈值处理的基础上,重构信号提高故障信号的信噪比;对重构信号进行经验模态分解,通过相关系数计算去除伪分量,并进行频谱分析。仿真信号和轴承故障信号的分析表明,该方法提高了信噪比,降低了EMD运算成本,减少了IMF的数量,保证了IMF的物理意义,成功完成微弱故障诊断。

Fault diagnosis of rolling bearing based on DCT and EMD

According to the traits of fault vibration signals from rolling bearing,a new method is presented for the fault diagnosis of the signals based on DCT（discrete cosine transform） and EMD（empirical mode decomposition）.Time series is reconstructed after threshold processing,in which the original time series is transferred to another corresponding coefficient vector based on DCT,the signal-to-noise ratio is improved.Then EMD is used to reconstructed signal.The false IMFs are removed after IMFs（intrinsic modal function） are analyzed by correlation coefficients.The spectrum of main component is then got.The results of simulated signal and the rolling bearing fault signal demonstrate that this method can improve signal-to-noise ratio,reduce arithmetic cost of EMD and the number of IMFs,and it can ensure the significance of IMF.The method can effectively diagnose the faults of bearing.