A Linearization Model of Turbofan Engine for Intelligent Analysis Towards Industrial Internet of Things

Abstract

Big data processing technologies, e.g., multi-sensor data fusion and cloud computing are being widely used in research, development, manufacturing, health monitoring and maintenance of aero-engines, driven by the ever-rapid development of intelligent manufacturing and Industrial Internet of Things (IIoT). This has promoted rapid development of the aircraft engine industry, increasing the aircraft engine safety, reliability and intelligence. At present, the aero-engine data computing and processing platform used in the industrial Internet of things is not complete, and the numerical calculation and control of aero-engine are inseparable from the linear model, while the existing aero-engine model linearization method is not accurate enough to quickly calculate the dynamic process parameters of the engine. Therefore, in this paper, we propose a linear model of turbofan engine for intelligent analysis in IIoT, with the aim to provide a new perspective for the analysis of engine dynamics. The construction of the proposed model includes three steps: First, a nonlinear mathematical model of a turbofan engine is established by adopting the component modeling approach. Then, numerous parameters of the turbofan engine components and their operating data are obtained by simulating various working conditions. Finally, based on the simulated data for the engine under these conditions, the model at the points during the dynamic process is linearized, such that a dynamic real-time linearized model of turbofan engine is obtained. Simulation results show that the proposed model can accurately depict the dynamic process of the turbofan engine and provide a valuable reference for designing the aero-engine control system and supporting intelligent analysis in IIoT.

Keywords

• Industrial Internet of Things
• multi-sensor data fusion
• cloud computing
• turbofan engine
• linearized model