摘要
为了能有效识别滚动轴承的故障信号,利用滚动轴承滚动体故障模型,构造相应的小波基;研究提升小波的预测器和更新器算法;利用小波基对故障特征信号敏感的特点,对轴承故障信号进行检测和分析.实验和仿真结果表明,利用提升小波对滚动轴承振动信号进行N层分解后,可在细节信号中容易地发现突变信号,再根据模极大值原理,有效地判断轴承故障是否存在;进一步对细节信号作Hilbert包络,检测功率谱中的故障特征频率,可准确判断滚动轴承滚动体是否存在损伤点.
In order to detect the fault signal of rolling bearings, wavelet bases were constructed based on the fault model. The lifting wavelet predictor and updater were researched. Bearing fault signal was detected and analyzed based on the characteristics of wavelet bases sensitive to fault feature signals. Experiments and simulation results showed that mutation signal can be easily found from detailed signals after Ndecomposition of the vibration signal of rolling bearings using the lifting wavelet. Bearing fault can be detected effectively by using the modulus maximum principle. The existence of fault points can be judged accurately by detecting the characteristic frequency of fault signals from the power spectrum after Hilbert envelope.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2009年第7期1218-1221,共4页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(50405045)
国家"863"高技术研究发展计划资助项目(2007AA04Z253)
上海市科技启明星计划资助项目(05QMX1455)
关键词
小波变换
滚动轴承
故障诊断
wavelet transform rolling bearings fault diagnosis
