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齿轮是机械设备的重要零部件之一。针对于齿轮副的在线故障检测问题,提出了一种基于经验小波变换的齿轮副故障检测方法。基于紧支撑的框架,该方法将信号通过经验小波变换分解为若干个不同的内部经验模态。较之传统经验模态分解,该方法能够更准确的提取出其模态信号,且混叠成分更小。将该方法应用于具体的齿轮副机构中,通过经验小波分解对系统噪声和环境噪声干扰中的振动信号进行故障特征识别。实验结果验证了该方法的有效性,能够有效的提取齿轮裂纹的特征信号。
Abstract：Gears are the key elements in industrial applications.Focus on the online fault diagnosis of gear pair,the paper presented a diagnosis method based on empirical wavelet transform(EWT).Based on compactly supported frames,the method decomposed the acquired signal into several different empirical modes via EWT.Compared with traditional empirical mode decomposition(EMD),the proposed method can extract the modal signals more accurately with less aliasing.An actual gear pair experiment is also carried out for the vibration signal fault extraction where the signal is submerged in the background noise and environment interferences.The experiment result shows that the proposed method is effective and can effectively reveal the gear crack characteristic signal.
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基本信息:
DOI：
中图分类号:TH132.41
引用信息:
[1]宋世毅.一种基于经验小波变换的齿轮副故障诊断方法[J].机械,2017,44(09):12-15.
基金信息:
2016年智能制造综合标准化与新模式应用项目(豫洛工业制造[2016]07744)