Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Abstract

The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

Exhaust Gas Temperature Prediction of Aero-Engine via Enhanced Scale-Aware Efficient Transformer

“…A careful selection of parameters was made from the extensive pool of over 200, focusing on those most indicative of gas path performance in the engine, as referenced in prior studies [13,21,29]. Scenario descriptors, including ALT, MN, PLA, and T0, are crucial in determining the flying state and are imperative for accurate Exhaust Gas Temperature (EGT) prediction.…”

“…With the boom in the development of machine learning and deep learning techniques, as well as the progress of sensor technology and real-time databases, the data-driven prediction of engine state parameters has attracted wide attention from academia and industry [12]. In their study on EGT prediction, Wang et al [13] established basic frameworks and employed several common machine learning methods. The analysis included the use of the Generalized Regression Neural Network (GRNN) network [14], the Radial Basis Function (RBF) network [15], Support Vector Regression (SVR) [16], and Random Forest (RF) [17].…”

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

“…The paper [61] proposes four data-driven frameworks for estimating the baseline exhaust gas temperature of aeroengines, a critical factor for engine health monitoring and flight safety. Among the tested machine learning models, the Generalized Regression Neural Network was highlighted for its superior accuracy and efficiency, suggesting its suitability for real-world airline deployment.…”

Artificial Intelligence in Aviation: New Professionals for New Technologies

“…The four data-driven frameworks to predict the exhaust gas temperature baseline of aeroengines, essential for engine health analysis and flight safety, are proposed in [23]. Using data from the real engine, machine learning methods were trained, with the Generalized Regression Neural Network model showing the highest accuracy and efficiency, making it suitable for practical airline applications.…”