基于多指标优化TQWT和TEO的轴承声发射故障诊断

Fault Diagnosis of Bearing AE Signal Based on Multi Index Optimization TQWT and Teager Energy Operator

针对滚动轴承早期故障声发射信号存在信噪比低、调制成分复杂导致故障特征难以识别的问题,提出一种利用多特征指标优化的可调Q因子小波变换(TQWT)和Teager能量算子(TEO)结合的故障诊断方法。以峭度-波形信息熵指标对TQWT参数(主要是品质因子Q)进行自适应选择,分解得到一系列子频带;然后,结合峭度、峰度、稀疏值组成融合指标对子频带进行筛选,对选出的子频带降噪后重构信号;最后求得重构信号Teager能量算子解调谱,通过对解调谱分析得到轴承故障特征信息。仿真和实验数据表明:该方法能在低转速强噪声背景下提取出轴承故障声发射信号中的冲击特征并进行故障诊断。

Aiming at the problems of low signal-to-noise ratio and complex modulation components of acoustic emission signal of rolling bearing early faults, a fault diagnosis method based on multi feature index optimized tunable Q factor wavelet transform(TQWT) and Teager energy operator(TEO) was proposed.The TQWT parameters(mainly quality factor Q) were adaptively selected by using the kurtosis-waveform information entropy index to decompose a series of subbands;combined with kurtosis, peak value and sparsity, the fusion index was composed to screen the subbands, and the signal was reconstructed after noise reduction;finally, the demodulation spectrum of the reconstructed signal Teager energy operator was obtained, and the bearing fault characteristic information was obtained by analyzing the demodulation spectrum.The simulation results and experimental data show that this method can be used to extract the impact characteristics of bearing fault acoustic emission signal under the background of low speed and strong noise to carry out fault diagnosis.