基于贝叶斯网络工况分类的民机引气系统异常检测

状态监控与健康管理技术是国产民机运营支持亟待突破的关键技术，以民机引气系统为对象，设计实现了基于飞行数据（QAR，Quick access recorder）的民机引气系统异常检测流程，提出了贝叶斯网络分类的同工况数据提取和指数加权滑动平均(Exponentially Weighted Moving Average，EWMA)控制图的引气系统异常检测方法。借助航空公司收集的实际数据对方法进行了验证，结果表明可有效检测民机引气系统异常，并提前故障发现时间，为航空公司合理安排飞机维修计划、减少飞机停场时间提供了支持。

Fault Detection for Civil Aircraft Bleed Air Systems Based on Bayesian Network State Classification

The technology of condition monitoring and health management is the breakthrough key technical of the domestic civil aircraft operators support. With civil aircraft bleed air system as the object of the research，a fault detection process based on flight data is designed and a method of fault detection for civil aircraft bleed air systems are proposed by the same state data extraction using bayesian network classification and exponentially weighted moving average control chart. The method is validated by using actual data collected in airline.The results show that the proposed method not only can detect fault for civil aircraft bleed air system, but also found fault ahead of time, which provides support for the airline to arrange aircraft maintenance programs reasonably, reduces aircraft downtime.