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    Advanced Multi-Variate Time Series Analytic Techniques (Attends)

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    ITC_2022_22-15-02.pdf
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    Author
    Lau, Richard
    Bagchi, Anindo
    Shen, John
    Triolo, Tony
    Sanchez, Kenneth
    Yao, Lihan
    Kovarskiy, Jacob
    Castro, Roberto
    Affiliation
    Peraton Labs, Test Resources Management Center
    Issue Date
    2022-10
    
    Metadata
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    Citation
    Lau, R., Bagchi, A., Shen, J., Triolo, T., Sanchez, K., Yao, L., Kovarskiy, J., & Castro, R. (2022). Advanced Multi-Variate Time Series Analytic Techniques (Attends). International Telemetering Conference Proceedings, 57.
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    URI
    http://hdl.handle.net/10150/666978
    Additional Links
    http://www.telemetry.org/
    Abstract
    We describe an advanced architecture supporting fast decisions by using multi-variate time series analytic techniques on voluminous datasets that were previously inaccessible. The system, Advanced Multi-Variate Time Series Analytic Techniques (ATTENDS) automates data ingestion, knowledge extraction, and Artificial Intelligence/Machine Learning (AI/ML) algorithm configuration for anomaly detection, failure prediction, causal analysis, and diagnosis. To enable reusability, ATTENDS presents a set of Application Programming Interfaces (API) to support user configurability and remote invocation. The APIs implement state-of-the art AI/ML algorithms for predictive maintenance, sensor component correlation for problem diagnosis, and unsupervised learning of sensor measurement anomaly for support of automated testing and evaluation. We will present two use cases including prediction of Remaining Useful Life (RUL) of Turbofan [1] and sensor diagnosis and recommendation for maintenance actions, as well as detection and quantification of target location error in an airborne platform.
    Type
    Proceedings
    text
    Language
    en
    ISSN
    1546-2188
    0884-5123
    0074-9079
    Sponsors
    International Foundation for Telemetering
    Collections
    International Telemetering Conference Proceedings, Volume 57 (2022)

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