摘要
针对工程实际中噪声干扰、不同源信号之间的混叠及信号的信噪比低 ,造成信号分析和特征提取难的问题 ,研究了采用连续小波变换 (CWT)和独立分量分析 (ICA)的方法对滚动轴承的声音信号进行了消噪和分离 ,从而提高了诊断信号的信噪比 ,保证了故障的确诊 .通过仿真实验和实例分析 ,验证了该方法的有效性 .
The essential objective of an engineering diagnostic system is to detect the potential faults existing in a continuously running machine. Complete and high quality diagnostic informa-tion is necessary for identifying the faults correctly. However, in practice, because of noise and the mixing of signals due to different components, the signal to noise ratio (SNR) of the signal picked up is usually low. Of course, this deeply affects signal analysis and feature extraction, and adds the difficulty of fault detection. In order to improve the quality of diagnostic signal and iden-tify the faults correctly, the continuous wavelet transform (CWT) and independent component analysis (ICA) are adopted together. Both simulation and example reveals the efficiency of this method.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2002年第3期295-299,共5页
Journal of Xi'an Jiaotong University
关键词
故障诊断
小波变换
盲源分离
独立分量分析
诊断信息
机械
fault diagnosis
wavelet transform
blind source separation
indepentent component analysis
