@inproceedings{0edfea2a179143679fecafebd55e28a8,
title = "Research on Fault Diagnosis Modeling Methods Driven by Route QAR Data",
abstract = "Diagnosing flight route faults based on QAR data is crucial for preventing flight accidents and reducing maintenance costs. This paper presents a method for diagnosing flight route faults using a Long Short-Term Memory Network and Decision tree algorithm (LSTM-DT). We used feature optimization algorithms to process the original QAR data, including rolling window operation and principal component analysis. The optimized features were then used to establish an LSTM anomaly detection model to identify abnormal points of the faulty route. We established a decision tree fault diagnosis model using these detected outliers to determine the corresponding fault types. When tested with real airline data, the fault diagnosis accuracy of this method reached 99.45%, confirming its effectiveness.",
keywords = "decision tree, fault diagnosis, LSTM, outlier detection, QAR",
author = "Yinlong Fang and Feng Jin and Nan Yang and Lu Yang and Guigang Zhang and Jian Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2024 ; Conference date: 26-07-2024 Through 28-07-2024",
year = "2024",
doi = "10.1109/SDPC62810.2024.10707770",
language = "English",
series = "Proceedings of 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "163--169",
editor = "Yongqiang Liu and Xiaohui Gu and Diego Cabrera and Baosen Wang and Mauricio Villacis and Chuan Li",
booktitle = "Proceedings of 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2024",
address = "United States",
}