多传感器系统中基于扩展卡尔曼滤波器的在线故障检测  

Online fault detection based on extended Kalman filter for multi- sensor system

作　　者：郭淑霞[1] 姜颖[1] 刘佳[1] 

机构地区：[1]河北工业大学计算机系,河北廊坊065000

出　　处：《传感器与微系统》2015年第6期126-129,共4页Transducer and Microsystem Technologies

基　　金：河北省科学技术研究与发展计划资助项目(14K50123D)

摘　　要：随着多传感器系统的广泛应用,在线故障对于系统性能影响严重,如何使得多传感器系统具有自主故障检测与诊断能力成为首要问题。根据非线性多传感器系统的输入信号、输出信号和故障阵列,建立一种具有多输入多输出处理和自调节加强功能的扩展卡尔曼滤波器(EKF)的故障分析模型,在此基础上,提出了一种适用于多传感器系统的在线故障检测算法及其在传感器节点上的实施架构。实验结果表明:所提算法在低并发故障和高并发故障环境下均具有高准确度故障报告能力。此外,在温度传感器上实施所提算法,温度监测值的对比结果验证了所提算法比传统算法具有更好的系统性能保证能力。With wide application of multi-sensor systems,online fault seriously affect system performance,so how to make multi-sensor system with independent fault detection and diagnostic capabilities become primary problem. According to input signal,output signal and fault array of nonlinear multi-sensor system,establish a kind of multi-input multi-output processing and fault analysis model of extended Kalman filter（ EKF ）with enhance self-regulating functions,on this basis,propose a kind of multi-sensor system online fault detection algorithm and its implementation framework on sensor node. Experimental results show that the proposed algorithm at low and high concurrent fault environments has highly accurate fault reporting capabilities. Also in contrast to the results of the temperature sensor to implement the proposed algorithm,the value of temperature monitoring verify the proposed algorithm has better system performance assurance capabilities than traditional algorithms.

关 键 词：扩展卡尔曼滤波器 多传感器系统 在线故障预测 系统性能保障 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]