摘要
针对高精度传感器硬件冗余成本巨大的问题,提出了不同精度的冗余传感器故障诊断方法.该方法采用动态模型不确定性影响最小化而故障影响最大化的原则,对低精度传感器数据进行预处理,轮流使用一个传感器作为输入,另一个作为输出建立卡尔曼滤波方程组,并通过所得新息进行故障诊断.实验表明,所提出方法能有效抑制低精度传感器的噪声干扰,降低成本以及系统建模复杂性,在传感器故障诊断的工程应用中具有较好的实用性.
To the problem of the huge cost in the high precision sensors hardware redundancy system, a different precision redundant sensor fault diagnosis method is proposed. By using the principle of minimizing uncertainties of the dynamic model and maximizing the impact of fault, firstly, the method reduces the noise and uncertainty by pre-processing low- precision sensor data, then takes turns using a sensor data as input, and the other one as output to establish the Kalman filter equations, and the innovation obtained is applied to the sensors fault diagnosis. The experiments show that, the method not only effectively suppresses the noise in low-precision sensors, but also can reduce costs and complexity of system modeling, which has obvious advantages in fault diagnosis of engineering application.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第12期1909-1912,共4页
Control and Decision
基金
国家自然科学基金项目(90820302)
国家博士点基金项目(200805330005)
NSFC面上(青年)项目(60805027)
湖南省院士基金项目(2009FJ4030)
