基于监测数据的特征小波构造及应用

Characteristic Wavelets Construction Based on Monitoring Data and Its Application

提出了以监测数据为基础构造特征小波提取数据趋势的新方法,研究了第二代小波变换的预测器、更新器与等效滤波器之间的关系,以及根据等效滤波器设计预测器和更新器系数的原理。为了构造基于监测数据的特征小波,在设计预测器和更新器时,综合监测数据样本的信息,以预测器消失矩作为约束条件,以预测误差作为目标函数,使所构造的小波能够反映监测数据的局部特征。采用设计预测器和更新器对监测数据分解、阈值处理和重构,得到监测数据的趋势。该方法在某炼油厂机组的峰峰值趋势分析中准确地描述了峰峰值变化趋势。

This paper put forward a new trend extraction method for monitoring data by constructing characteristic wavelet based on monitoring data. Investigating the relation between predictor and updater of second generation wavelet transform and their equivalent filters, predictor and updater are designed on the basis of the equivalent filters. In order to get the characteristic wavelet, the information of the monitoring data is taken into account. Then considering vanishing moment number of predictor as constraint condition, and regarding prediction error as an objective function, the characteristic wavelet constructed can represent the localized characteristic of the monitoring data. By using the devised predictor and updater for second geueration wavelet transform composition, threshold processing and reconstruction, the data trend can be obtained. The proposed method nicely representes the trend of monitoring data from a machine set in an oil refinery. 2 figs, 7 refs.