Aircraft Fleet Health Monitoring with Anomaly Detection Techniques
نویسندگان
چکیده
Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring prognostics for scheduling condition-based operations is still very challenging. The increasing operational data along with progress made machine learning boosted development data-driven management (PHM) models. this paper, we describe workflow place at an airline aircraft highlight difficulties related to a proper labelling status such systems, resulting poor suitability supervised techniques. We focus on investigating feasibility potential semi-supervised anomaly detection methods real system. Proposed evaluated large volumes sensor from cooling unit modern wide body European airline. For sake confidentiality, been anonymized only few technical details about had available. trained several deep neural network autoencoder architectures nominal used scores calculate indicator. Results suggest that high correlated identified failures logs. Also, some situations see increase score flights prior system’s failure, which paves natural way early fault identification.
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ژورنال
عنوان ژورنال: Aerospace
سال: 2021
ISSN: ['2226-4310']
DOI: https://doi.org/10.3390/aerospace8040103