摘要
噪声是影响机械设备早期故障预示正确性的主要因素。根据相关分析理论和小波阈值降噪理论,提出了小波域相关滤波法。它是一个迭代的过程,通过自适应的选择滤波过程参数,可以对信号进行良好的降噪;同时也不会弱化信号中的弱故障信息。通过对旋转机械早期不平衡和不对中故障信号分析,结果表明小波域相关滤波法对机械设备早期故障预示是有效的。
Noise is the biggest obstacle that makes the incipient fault prognosis results uncorrected. According to the theories of correlation analysis and threshold de-noising by wavelets, wavelet transform domain correlation filter (WTDCF) is constructed. WTDCF is an iterative process. By selecting the process parameters adaptively, WTDCF can de-noise signal efficiently. More important, the faint component in the signal will become stronger compared with the noise component. WTDCF method is used to analyze the real signals collected from a bearing that has incipient unbalance and misalignment faults. Results show that WTDCF method is effective for bearing incipient fault prognosis.
出处
《振动工程学报》
EI
CSCD
北大核心
2005年第2期145-148,共4页
Journal of Vibration Engineering
基金
国家自然科学基金重点资助项目(50335030)
关键词
故障诊断
小波变换
相关滤波法
信号降噪
故障预测
旋转机械
Bearings (machine parts)
Correlation theory
Iterative methods
Noise abatement
Signal filtering and prediction
Spurious signal noise
Wavelet transforms
