Aero-engine on-board model based on big Quick Access Recorder data

Abstract

With the development of the Full Authority Digital Engine Controller (FADEC) technology, the aero-engine on-board model is widely used in Engine Health Management (EHM) and control. Due to the FADEC’s limited computational capability and storage capacity, the model should not be very intricate; consequently, the interpolation model is widely utilized. Although the interpolation model’s low precision precludes further development of on-board models for EHM and control. To address the trade-off between precision and complexity, a novel on-board modeling method is proposed based on the Nonlinear Autoregressive with Exogenous Inputs Backpropagation neural network (NARX-BPNN) trained using the mini-batch Levenberg–Marquardt (LM) algorithm on large Quick Access Recorder (QAR) data. The NARX model’s features and time delay are chosen by referring to the line interpolation model, which gives interpretability for feature selection. The combination of a shallow neural network and big data training can guarantee the on-board model’s real-time and storage requirements, as well as its generalizability. The mini-batch LM method can avoid both the local optimum problem in the shallow neural network and the storage difficulty associated with massive data while still achieving a rapid convergence rate due to the LM algorithm’s global view. The NARX-BPNN models are compared to an existing line interpolation model using 100 different aero-engines' QAR data. The results reveal that accuracy may be increased by approximately 30% while maintaining superior dynamic performance and anti-noise capacity compared to the line interpolation approach.

Risk assessment method for controlled flight into terrain of airlines based on QAR data

“…Currently, scholars use QAR data to conduct research primarily in the following domains: Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al , 2013; Sembiring et al , 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al , 2022; Huang et al , 2019; Pan et al , 2021), identification of normal and abnormal flights during operation (Li et al , 2011), identification of anomalous in-flight data (Chen et al , 2022; Jesse et al , 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al , 2022), resolution of the similarity issue in QAR data (Feng et al , 2012), diagnosis of aircraft exceedance event (Gao et al , 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012; Yang and Meng, 2012). Diverse data analysis systems are developed to explore operational risks, evaluate and assess pilot flight technology, identify technical training problems, optimize training processes, enhance flight training quality, monitor flight quality, engine status and elevate the underutilized QAR data rate (Haverdings and Chan, 2010; Sun, 2003; Li, 2010).…”

“…Currently, scholars use QAR data to conduct research primarily in the following domains: Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al, 2013;Sembiring et al, 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al, 2022;Huang et al, 2019;Pan et al, 2021), identification of normal and abnormal flights during operation (Li et al, 2011), identification of anomalous in-flight data (Chen et al, 2022;Jesse et al, 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al, 2022), resolution of the similarity issue in QAR data (Feng et al, 2012), diagnosis of aircraft exceedance event (Gao et al, 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012;Yang and Meng, 2012).…”