快速路径优化引导ACMD的变转速轴承故障诊断

Fault Diagnosis of Rolling Bearing Based on Fast Path Optimization Combined with ACMD Under Variable Working Conditions

针对变转速工况下滚动轴承故障特征识别困难的问题,提出了一种快速路径优化算法(FPO)引导自适应线性调频模态分解(ACMD)的变转速轴承故障诊断方法。首先,对轴承故障信号进行希尔伯特变换解调提取隐藏在高频信号中的故障信息;其次,采用FPO算法从信号的时频分布中对信号分量的瞬时频率进行初始估计;将预估的各分量初始频率作为ACMD的初始参数对原始包络信号进行分解;最后,根据分解得到各个信号分量的瞬时频率和瞬时幅值等信息构建出高分辨率的时频表示。通过分析实测信号表明,所述方法能够展示出各个信号分量的幅值和频率变化趋势,剔除了无关成分的干扰,清晰地演示变转速工况下轴承故障信号的时变特征。

Aiming at the difficulty in identifying the fault features of rolling bearings under variable speed conditions, a fast path optimization algorithm(FPO) guided adaptive chirp mode decomposition(ACMD) method for variable speed bearing fault diagnosis was proposed.First, the bearing fault signal is demodulated by Hilbert transform to extract the fault information hidden in the high frequency signal;then the FPO algorithm is used to initially estimate the instantaneous frequency of the signal component from the time-frequency distribution of the signal;The component initial frequency is used as the initial parameter of ACMD to decompose the original envelope signal, and finally a high-resolution time-frequency representation is constructed according to the instantaneous frequency and instantaneous amplitude of each signal component obtained from the decomposition.Analysis of the measured signal shows that the method can eliminate the interference of irrelevant components and clearly represent the time-varying characteristics of the bearing fault signal under the condition of variable rotational speed.