摘要
选用Morlet小波基函数,将连续小波变换分别应用于时域同步平均信号和齿轮啮合残余信号。对比分析了正常齿轮、微小裂纹齿轮和破损齿轮的小波功率谱和最大小波功率谱,发现齿轮啮合残余信号的小波功率谱和最大小波功率谱对微小裂纹较敏感,能较早地诊断齿轮裂纹的出现。提取小波功率最大值及其与平均值的比值,作为诊断齿轮裂纹出现和进展的量化指标,并作了分析验证。对比分析了齿轮振动时域同步平均信号和啮合残余信号的时域波形和傅立叶频谱,均不能及时诊断微小裂纹。
The continuous wavelet transform using Morlet function is used to analysis both time synchronous averaging signal and gear motion residual signal.The scalogram graph and the maximum scalogram over sampling points is analyzed.It is demonstrated that the scalogram graph of gear motion residual signal is sensitive to gear fault and can correctly indicate early crack.The maximum scalogram and its ratio to average scalogram value are presented to evaluate gear fault advancement quantitatively.By contrast,both the time domain signal and FFT frequency spectrum are also analyzed,and demonstrated not to be able to indicate the occurrence of small crack in gears.
出处
《制造技术与机床》
CSCD
北大核心
2011年第5期63-70,共8页
Manufacturing Technology & Machine Tool
基金
人事部留学人员科技活动择优资助项目(DB200903036)
江西省科技支撑计划项目(2009BGB02800)
关键词
齿轮传动
故障诊断
连续小波变换
啮合残余信号
时域同步平均信号
Gear Transmission
Fault Diagnosis
Continuous Wavelet Transform （CWT）
Gear Motion Residual Signal
Time Synchronous Averaging Signal
