For the civil aviation industry, QAR provides a gold mine of data for a wide range of applications. The data quality is an essential precondition for its usage. Thus, to detect the abnormalities or outliers is an important procedure for its further utilization. In this study, we adopted four approaches to detect the abnormalities, i.e. Pauta criterion, boxplot, K-means algorithm and LOF algorithms. Results show that these approaches perform differently for various parameters in a QAR data set. The Pauta criterion seems to be not working well for most of the QAR parameters. In contrast, the K-means clustering and LOF algorithms generally work well with the selected QAR parameters.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Detecting Anomalies in Quick Access Recorder Data


    Contributors:
    Wu, Binwen (author) / Zhang, Xiaoyue (author) / Xiao, Ming (author) / Sun, Huabo (author) / Lu, Binbin (author)


    Publication date :

    2022-10-12


    Size :

    1020565 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Aircraft Mass Estimation using Quick Access Recorder Data

    He, Fang / Li, Lishuai / Zhao, Weizun et al. | IEEE | 2018


    Quick access recorder for AIDC system (QAR)

    Airlines Electronic Engineering Committee | TIBKAT | 1972




    Reconstruction of Aircraft States During Landing Based on Quick Access Recorder Data

    Höhndorf, Lukas / Siegel, Joachim / Sembiring, Javensius et al. | AIAA | 2017