Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China.
Sensors (Basel). 2011;11(1):260-77. doi: 10.3390/s110100260. Epub 2010 Dec 28.
A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm l(p) of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method.
本研究提出了一种非线性冗余提升小波包算法。对于要在不同层分解的节点信号，选择具有不同消失矩阶数的预测算子和更新算子，对分解得到的尺度系数和小波系数进行范数 l(p)的计算，选择与最小范数值对应的预测算子和更新算子作为最优算子，以匹配节点的信息特征。针对冗余提升小波包分析中存在的频率混叠和频带交错问题，提出了一种改进的分解和节点单分支重构算法。计算最底层分解节点系数的归一化能量，提取能量最大的节点信号进行解调。将改进的非线性冗余提升小波包分析应用于某厂精轧机滚子轴承的故障诊断中，成功检测到了滚子轴承的故障。该应用证明了该方法的有效性和实用性。
