Abstract: Accurate fault diagnosis of airborne system is an important way to ensure safe aircraft flight and maximize economic benefits. However, due to impacts of internal and external environmental conditions, the sensor has detection uncertainties, and is thus difficult to guarantee the validity and correctness of fault diagnosis based on detection of single sensor or of local sensors. Based on the structure and working principle of the airborne system, this paper proposes a hierarchical diagnosis decision-making method based on a combination of evidence forward fusion and reverse verification. The correlation between the detection features of sensors in different levels and different regions in the airborne system, and the uncertainty of a single sensor itself is considered. Estimation of both of the system state and sensor state can be realized, and the influence of single sensor fault on the accuracy of system diagnosis can be overcome. Compared with the traditional fault diagnosis model, the proposed method no longer depends on the absolute reliability of one or a kind of sensor information. At the same time, it can realize the accurate fault diagnosis at the system level, and can also judge whether a specific sensor itself has virtual alarm. In the case of fault diagnosis of aircraft hydraulic system, it can be seen that with the method proposed, the false alarm rate of system fault diagnosis is successfully reduced by 96% and the sensor uncertainty by 84%.

Key words: sensor false alarm, evidence fusion, airborne system, cross check between layers, Bayesian network