基于神经网络的非线性时间序列故障预报  

Fault Prediction for Nonlinear Time Series Based on Neural Network

作　　者：胡寿松[1] 张正道[2] 

机构地区：[1]南京航空航天大学自动化学院,南京210016 [2]江南大学通信与控制工程学院,无锡214122

出　　处：《自动化学报》2007年第7期744-748,共5页Acta Automatica Sinica

基　　金：国家自然科学基金重点项目(60234010);国防基础科研项目(K1603060318);航空科学基金(05E52031)资助~~

摘　　要：对模型未知非线性系统,将系统输出组成时间序列并通过空间嵌入的方法转化为一个离散动态系统．利用线性AR模型拟合时间序列的线性部分,用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动,提出了通过对状态的观测实现时间序列一步预测的方法．利用滚动优化的思想将一步预测推广,提出了时间序列的N步预测方法,证明了时间序列预测误差有界．通过对预测误差进行概率密度估计和检验,提出了故障的预报方法．对F-16歼击机的结构故障预报结果表明了方法的有效性．According to the Takens embedding theorem, the nonlinear time series combined with system output is converted into discrete dynamic system. An autoregressive model is used to fit the linear part of series; the neural network is used to fit the nonlinear part of series and to compensate for the unknown disturbance. The prediction of time series is achieved by the observation of system states. So a one-step prediction method is proposed. Using the so-called moving horizon optimization method, one-step prediction is extended to N-steps prediction. The boundedness of prediction error is proved. The fault is predicted by the prediction error through density function estimation and hypothesis test. The simulation of the structure fault prediction of a fighter F-16 proved the efficiency of the model.

关 键 词：非线性时间序列 神经网络 滚动预测 概率密度估计 故障预报 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]