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

doi:10.3969/j.issn.0255-8297.2016.01.011

应用科学学报 ›› 2016, Vol. 34 ›› Issue (1): 95-105.doi: 10.3969/j.issn.0255-8297.2016.01.011

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

郇滢, 兰惠清, 林楠, 张平

北京交通大学机械与电子控制工程学院, 北京 100044

收稿日期:2015-03-07 修回日期:2015-05-26 出版日期:2016-01-30 发布日期:2016-01-30
通信作者: 兰惠清,教授,博导,研究方向:管道安全监测、检测,E-mail:hqlan@bjtu.edu.cn E-mail:hqlan@bjtu.edu.cn
基金资助:
国家科技支撑计划重点项目基金(No.2011BAK06B01)资助

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

HUAN Ying, LAN Hui-qing, LIN Nan, ZHANG Ping

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2015-03-07 Revised:2015-05-26 Online:2016-01-30 Published:2016-01-30

摘要/Abstract

摘要： 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.

关键词: 悬索管桥, 应变预测, 小波变换, GM(1, 1)-ARMA

Abstract: 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.

Key words: suspension pipeline, strain prediction, wavelet transform, GM(1,1)-ARMA

中图分类号:

TE832

郇滢, 兰惠清, 林楠, 张平. 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[J]. 应用科学学报, 2016, 34(1): 95-105.

HUAN Ying, LAN Hui-qing, LIN Nan, ZHANG Ping. Prediction of Suspension Pipeline Strain by GM(1,1)-ARMA Model Based on Wavelet Transform[J]. Journal of Applied Sciences, 2016, 34(1): 95-105.

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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

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

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