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Received: 30 March 2019 Accepted: 21 June 2019 The fuel flow is a key indicator of the performance of aircraft engine. It helps to identify the performance degradation and failure of the engine. This calls for an aircraft engine model that can predict the fuel flow accurately throughout the flight, especially the climb phase. This paper performs stepwise linear regression of the data collected by a quick access recorder (QAR), and creates a model of the fuel flow of Boeing 737-700 in the climb phase. Firstly, the possible influencing factors of fuel flow were screened based on scatterplots and Pearson correlation coefficients (PCCs). Next, the selected factors were further modified and screened through similarity correction and power correction. On this basis, the fuel flow model for the climb phase was established through stepwise linear regression and corrected in the light of the tolerance and variance inflation factor (VIF) of each variable. The prediction results of the final model were basically in line with the actual QAR data.

Modelling of Fuel Flow in Climb Phase Through Multiple Linear Regression Based on the Data Collected by Quick Access Recorder