2025/04/12 by Yilin Wang, Wang, Yilin, Lei, Peixuan +12 · 2 citations
Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #FOS: Electrical engineering #Fault Detection and Control Systems #Signal Processing (eess.SP) #Time Series Analysis and Forecasting #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2504.09090
openalex publication_date 2025/04/12 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
Bleed Air System (BAS) is critical for maintaining flight safety and operational efficiency, supporting functions such as cabin pressurization, air conditioning, and engine anti-icing. However, BAS malfunctions, including overpressure, low pressure, and overheating, pose significant risks such as cabin depressurization, equipment failure, or engine damage. Current diagnostic approaches face notable limitations when applied across different aircraft types, particularly for newer models that lack sufficient operational data. To address these challenges, this paper presents a self-supervised learning-based foundation model that enables the transfer of diagnostic knowledge from mature aircraft (e.g., A320, A330) to newer ones (e.g., C919). Leveraging self-supervised pretraining, the model learns universal feature representations from flight signals without requiring labeled data, making it effective in data-scarce scenarios. This model enhances both anomaly detection and baseline signal prediction, thereby improving system reliability. The paper introduces a cross-model dataset, a self-supervised learning framework for BAS diagnostics, and a novel Joint Baseline and Anomaly Detection Loss Function tailored to real-world flight data. These innovations facilitate efficient transfer of diagnostic knowledge across aircraft types, ensuring robust support for early operational stages of new models. Additionally, the paper explores the relationship between model capacity and transferability, providing a foundation for future research on large-scale flight signal models.