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.