Abstract
Engine fuel flow is one of the most important parameters of aero-engine performance, which can accurately reflect the actual condition of aero-engine. It'is possible to monitor the aero-engine fuel flow (FF) by reading the data of QAR (Quick Access Recorder). However, it's difficult to monitor the actual condition of aero-engine through the traditional method because of the vast amount of QAR data and the complexity of the engine itself. Feed forward process neural network is adopted to monitor the QAR-based aero-engine fuel flow . The results of the simulation are acceptable and show that the Neural Net model is an effective method to monitor aero-engine conditions.
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