基于小波变换的GM(1,1)-ARMA组合预测模型对悬索管桥的应变预测

ARMA模型采用差分处理对桥梁监测数据进行预测时,会出现数据丢失和预测精度降低的现象.为此,利用小波变换对信号进行离散化处理信息不会丢失的优点,将趋势明显的原始序列离散化,得到不同频带上的块信号.采用灰色GM(1,1)模型对趋势明显的低频信号进行趋势预测,用ARMA模型对平稳的高频细节信号进行细节预测,再将两部分预测值叠加得到最终预测值.对黄河悬索管桥在线监测系统获得的过去一段时间的应变数据进行验证,结果表明所提出的GM(1,1)-ARMA组合模型预测效果明显高于传统ARMA模型,这对实现同类桥梁的预警具有积极意义.

Prediction of Suspension Pipeline Strain by GM(1,1)-ARMA Model Based on Wavelet Transform

When using an ARMA model to predict monitoring data of bridges, difference processing causes some data lose. To improve prediction accuracy, this paper makes use of the advantages of wavelet analysis, i.e., no information is lost after wavelet transform. The time series with a clear trend are divided into two parts. The low-frequency part representing strain trend is modeled using GM(1,1), and the high-frequency part representing random disturbance using ARMA. The predicted value is then obtained by combining the two parts. Validation is made with the strain data acquired from an on-line monitoring system on a Yellow River suspension bridge. The results show that prediction accuracy of the combined GM(1,1)-ARMA model is higher than the traditional ARMA. The method is applicable to early warning of similar bridges.