Principal component analysis (PCA) was used to detect anomalies in wind tunnel measurements. Data were compiled from previous measurements for three two-dimensional airfoils in the Virginia Tech Stability Wind Tunnel with similar experimental arrangements. Measurements included the surface pressure distribution about the airfoil, total pressure distribution in the airfoil wake, static pressure in the wake, angle of attack, freestream velocity, flow temperature, and ambient pressure. These data were used to train the PCA scheme through an eigendecomposition of the measurement covariance matrix. A low-order reconstruction of this covariance matrix was then used to assess whether new measurements not included in the training set were anomalous by comparing predictions of the expected result with measured values. Results show that the method is very good at detecting anomalies in data from airfoils included in the training set as well as an additional airfoil outside of this set. Predictions of a particular measurement can be further improved by biasing the data used to construct the covariance matrix with observations that are more similar to the measurement of interest. However, this biasing reduces the ability of the method to predict results and therefore detect anomalies for experimental conditions outside of the range of the input data.


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    Titel :

    Anomaly Detection in Wind Tunnel Experiments by Principal Component Analysis


    Beteiligte:

    Erschienen in:

    AIAA Journal ; 60 , 4 ; 2297-2307


    Erscheinungsdatum :

    2021-12-22


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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