基于自适应最优Morlet小波的滚动轴承故障诊断
Method of Incipient Fault Diagnosis of Bearing Based on Adaptive Optimal Morlet Wavelet

滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖，增加了对滚动轴承故障诊断的难度。针对这一问题，笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先，利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优，在获得最优Morlet小波的同时保证了良好的带通滤波性能；然后，将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪，提高信号的信噪比；最后，对最优Morlet小波滤波信号进行包络谱分析，通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明，笔者所提方法能够有效提取故障信号中的特征信息，具有一定的有效性。
The early stage weak impulsive fault feature is so weak that it is always covered by environmental noise, which increases the fault diagnosis difficulty of rolling bearing. Aiming to this problem, a new diagnosis method based on adaptive optimal Morlet wavelet transform is proposed. Firstly, The core parameter of Morlet wavelet transform is calculated by particle swarm optimization (PSO) adaptively, which guarantees optimal Morlet based wavelet as well as wonderful band-pass filter performance; Secondly, in order to improve signal-to-noise ratio, optimal Morlet wavelet is used to filter incipient fault signal of rolling bearing; Finally, optimal Morlet wavelet filtered signal is analyzed by envelope spectrum, and the fault location of rolling bearing is extracted by contrasting the major frequency with the fault frequency of rolling bearing. The analysis results of simulated signal and measured signal show that the proposed method is able to extract the fault impulse signal.