Corpus ID: 218719368

An Incremental Clustering Method for Anomaly Detection in Flight Data

@article{Zhao2020AnIC,
  title={An Incremental Clustering Method for Anomaly Detection in Flight Data},
  author={Weizun Zhao and Lishuai Li and Sameer Alam and Yanjun Wang},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.09874}
}
  • Weizun Zhao, Lishuai Li, +1 author Yanjun Wang
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred as black box data on aircraft, has gained interest from researchers, airlines, and aviation regulation agencies for safety management. New anomaly detection methods based on supervised or unsupervised learning have been developed to monitor pilot operations and detect any risks from onboard digital flight data recorder data. However, all existing… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 36 REFERENCES

    Advisory Circular

    • Federal Aviation Administration.
    • Washington, D.C. 20590. April 4, 1972. 7p. Journal of Travel Research, 12(2), 28-28. doi:10.1177/004728757301200242
    • 1973

    Atypical event and typical pattern detection within complex systems

    VIEW 1 EXCERPT