第2代小波变换及其在机电设备状态监测中的应用

Second Generation Wavelet Transform and Its Application to Mechanical Monitoring

为了解决最佳小波基函数选择的问题,采用第2代小波变换的方法构造小波,从振动信号中提取机电设备故障信息.第2代小波变换与经典小波变换不同,它不依赖Fourier变换,所有的运算在时域上进行,通过设计预测算子和提升算子可以构造具有某种特性的小波.针对机电设备的状态监测和故障诊断,阐述了一种离线设计预测算子和提升算子的方法,通过求解线性方程组确定预测系数和提升系数,并在此基础上构造基于插值细分方法的第2代小波变换算法.在某炼油厂机组的状态监测和故障诊断中,采用该算法有效地提取了轴系不对中的故障信息.

In order to fix the problem of selecting an optimum wavelet basis, the second generation wavelet transform (SGWT) is employed to construct the wavelet for extracting the fault information of mechanical equipment from vibration signals. SGWT is different from the classical wavelet transform: not relying on the Fourier transform (FT), doing all the calculations on time-domain, and constructing the special property wavelet by designing predictor and updater. A design method of predictor and updater is described for the mechanical equipment monitoring and fault diagnosis. Coefficients of predicting and updating are gained by solving linear equation sets, and then the algorithm based on the interpolating subdivision method is found by using these coefficients. The fault information of shafting misalignment is efficiently extracted by applying the algorithm in the mechanical equipment monitoring and fault diagnosis of a refinery.