小波包络解调在轧机故障早期诊断中的应用

针对轧钢机振动信号频谱成分的多样性和高低频混杂性，利用小波正交分解的多尺度多频带特性将振动信号展开到一系列相邻的小波空间上，使调制现象突显出来易于分析；再借助希尔波特包络分析技术对小波分解后的包含调制信号的分量进行解调，成功地提取出了故障早期特征。通过后期的振动数据跟踪分析，发现轧机轴承的外圈、滚动体相继出现损伤，并逐渐恶化，与停机开箱后结果基本吻合，进一步证实了小波包络解调技术的有效性。说明小波分析和包络解调技术能够有效地提取轧机轴承故障早期的特征，为设备的早期维修决策提供了重要的依据，同时可以避免恶性事故的发生。

Application of Wavelet Envelope Demodulation on Early Fault Diagnosis of the Rolling Mill

For the multi-kinds and complex of rolling mill vibration signal frequency components, the vibration signal was decomposed into a series of adjoining wavelet space using wavelet orthogonal transform with its multi - scale and frequency band characteristic ,which made the modulation obvious and easy to analyze. Then the early fault characteristic on inner race was successfully extracted from the decomposed signal using the Hilbert envelope demodulation technique. The defect aggravation of out race and roller was detected through the trend analysis of later signals, which is basically identical with the real phenomena existing in gearbox. The wavelet envelope demodulation technique was proved to be effective in early fault diagnosis . It demonstrated that wavelet analysis and envelope demodulation technique could perfectly extract the early fault characteristic of rolling mill bearing and provide essential information for the early maintaining plan, avoiding the emergence of heavy damage.