Resumen
Traditional methods are unable to effectively assess the health status of engine bleed air systems. To address the limitation, this paper proposes a methodology for constructing health indicators using multi-level feature extraction. First, this approach involves data-level feature extraction from Quick Access Recorder (QAR) data and employs a method of significance compensation to process the QAR data. Second, through unsupervised learning, the ResNet Deep Autoencoder (RDAE) is utilized to do the feature-level feature extraction from the processed data. This can solve the problem of lacking annotated data and obtain the health indicators of the engine bleed air system. Third, the method was experimented on one year of QAR data from a specific airline company. The results demonstrate that the RDAE approach achieves the best performance in constructing health indicators for the system. It achieves a miss rate of 0.0523 for the duct pressure of 5th stage bleed, reducing the miss rate by 0.2810 compared to Kernel Principal Component Analysis (KPCA). It also achieves a miss rate of 0 for the pre-cooler outlet temperature, reducing the miss rate by 0.0035 compared to the Deep Autoencoder (DAE). The results indicate that the proposed method provides a more effective assessment of the health status of the engine bleed air system.