Abstract：When background noise changes large, it is difficult to diagnose the failure of multi-precision redundancy sensors system. In order to address this problem, a method based on two stage Kalman filter(TSKF) is proposed. Firstly, the noise estimated by wavelets, and the sensor data is preprocessed by strong tracking Kalman filter, which is to reduce the uncertainty of observation and maximize fault information, and then it takes advantage of the redundancy to using a sensor as input, and the other sensors as output to establish the Kalman filter equations, by which the innovation obtained is used to the sensors fault diagnosis. The relationship between fault detection rate and the strength of noise is discussed by experiments, which shows that the method is robust and improve the accuracy of fault diagnosis.
谭平, 蔡自兴. 强噪声背景下的多精度传感器故障诊断[J]. 电工技术学报, 2012, 27(4): 83-87.
Tan Ping, Cai Zixing. Fault Diagnosis of Multi-Precision Sensors in Large Noise Background. Transactions of China Electrotechnical Society, 2012, 27(4): 83-87.
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2012, Vol. 27 
