摘要
基于小波降噪和盲源分离相结合对机械信号进行分离与故障诊断。首先使用经分析选择的较好小波阈值对非平稳振动信号进行降噪,然后运用盲源分离技术分离出激振信号,结果表明利用小波阀值降噪后进行盲源分离时分离信号与源信号相似系数优于直接盲源分离;将小波降噪和盲源分离相结合应用于某燃气轮机的实测故障信号提取,诊断出转子发生了不平衡及碰摩等故障现象,与实测情况相符,有效说明了该方法在旋转机械故障诊断中的实用性。
The vibration signals of rotating machinery are separated and diagnosed by combining the wavelet noise reduction and the blind source separation in this paper. Firstly, the combing method uses the better wavelet threshold value de-noising to reduce noise for non-stationary vibration signals, and then separates the useful vibration signals with blind source separation. It shows that the combining method is more effective than the direct blind source separation in signal processing. Applying the combining method to analyze real measured trouble signals of a gas turbine, the fault diagnosis results are found to be in agreement with practice. The result shows that the combing method is efficient in analyzing the fault diagnosis of rotating machinery.
出处
《机械科学与技术》
CSCD
北大核心
2012年第5期726-730,共5页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(10902084)
陕西省自然科学基础研究项目(2011JQ1011)
航空科学基金项目(20092108003
20112108001)
西北工业大学2010年度"翱翔之星计划"项目资助
关键词
故障诊断
盲源分离
小波阀值
旋转机械
fault diagnosis
blind source separation
wavelet threshold
rotating machine
