基于智能信息融合的飞控系统故障检测隔离方法研究  

Failure Detection and Isolation in Flight Control System Based on the Intelligent Data Fusion Technology

作　　者：李正强[1] 张怡哲[1] 李陶[1] 邓建华[1] 

机构地区：[1]西北工业大学航空学院,陕西西安710072

出　　处：《飞行力学》2009年第2期85-88,共4页Flight Dynamics

摘　　要：利用小波时频信号分析技术来分离非平稳噪声,提高了残差中故障信息的信噪比。提出了智能信息融合故障检测新方法,应用模糊逻辑自适应调节无故障的模型参数,抑制残差中非平稳信号的增长;采用人工神经网络故障分类器来消除非线性故障信号偏差对残差判决的影响,扩大了故障检测与隔离算法的适用范围。仿真结果表明,该算法技术性能优越,改进效果明显。In order to enhance the signal-to-noise ratio of weak information of residual error,the wavelet frequency-signal analyzing technology is adopted to separate the non-steady noise.The new failure detection algorithm with intelligent information fusion is proposed,in which the model parameter is automatically adjusted in non-failure with fuzzy logic,then the non-steady signal＇s growth of residual error is suppressed.The artificial neural network is used to eliminate the influence of the non-linear deviation signal on the residual error decision,so the applicable scope of algorithm with failure detection and isolation is expanded.The simulation results indicate that,this algorithm＇s technical performance is superior,and the improvement effect is obvious.

关 键 词：自修复飞控系统 小波分析 故障检测与隔离 数据融合 人工神经网络 

分 类 号：V249[航空宇航科学与技术—飞行器设计]