Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


The development of an automated health monitoring framework is critical for aviation system safety, especially considering the expected increase in air traffic over the next decade. Conventional approaches such as model-based and exceedance methods have a low detection accuracy and are limited to specific applications. This paper proposes a robust real-time health monitoring framework for detecting performance anomalies, which may impact system safety during flight operations, with high accuracy and generalized applicability. The proposed monitoring framework utilizes sensor data from commercial flight data recorders to predict possible flight performance anomalies. Decimation, a signal processing technique, in conjunction with Savitzky-Golay filtering is utilized to preprocess the dataset and mitigate sampling rate and noise issues that prevent direct usage of historical flight data. Correlation-based feature subset selection is subsequently performed, and these features are used to train a support vector machine that predicts flight performance. With this model, performance anomalies in the test data are automatically detected based on deviations from the predicted flight behavior. The proposed monitoring framework was demonstrated to detect performance anomalies in real-time and exhibited accurate detection capabilities with high computational efficiency.

Original languageEnglish (US)
Article number101071
JournalAdvanced Engineering Informatics
StatePublished - Apr 2020


  • Anomaly detection
  • Aviation safety
  • Feature selection
  • Real-time monitoring
  • Support vector machine

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence


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