Feature extraction of AE signal for rolling bearing fault by improved TFCA method
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摘要:
针对时频相干方法(time-frequency coherence analysis,TFCA)不能分析单通道信号的问题,提出了一种新的时频相干方法,通过平均估计算子实现对单通道信号分析。利用软岛全息声发射系统和旋转机械模拟平台进行了滚动轴承内圈、外圈故障声发射(acoustic emission,AE)检测,研究了低信噪比条件下滚动轴承故障特征周期提取。将所提方法与传统时频相干、短时傅里叶变换、小波相干方法进行对比研究,并进行实验验证。仿真和实验结果表明:所提方法明显优于其他时频方法,时频聚集性高、抗噪性能强,并能准确提取轴承故障周期特征。
Abstract:In order to solve the problem of traditional time-frequency coherence analysis (TFCA) method's failure to analyze the single-channel signal, a new time-frequency coherence method was proposed. The new method can analyze the single-channel signal through the average estimation operator. The soft-land full-information acoustic emission (AE) system and the rotating machinery simulation platform were used to detect the inner ring and outer ring faults of the rolling bearing. The study focused on the extraction of fault characteristic periods of rolling bearings under conditions of low signal-to-noise ratio. The proposed method was compared with the traditional time-frequency coherence, short-time Fourier transform, wavelet coherence methods, and was verified experimentally. The simulated and experimental results showed that the proposed method is superior to other time-frequency methods. The proposed method can accurately extract the period characteristics of fault signal, and exhibit high time-frequency aggregation and strong anti-noise performance.
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表 1 时频聚集度表
Table 1. Time-frequency aggregation table
不同方法 Rényi熵值 传统时频相干方法 1.01 本文方法 0.94 短时傅里叶变换 2.10 小波相干 0.93 -
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