Described are systems and methods relating to the causal detection and diagnosing of faults and anomalous operation of autonomous vehicles, such as unmanned aerial vehicles (UAVs), using machine learning. Embodiments of the present disclosure can provide systems and methods for detecting and diagnosing faults based on comparisons between the measured operation and/or behavior of a vehicle to the vehicle's expected nominal operation and/or behavior. Accordingly, the systems and methods according to embodiments of the present disclosure do not require prior knowledge of faults or modeling of the vehicle, the vehicle's operation, and/or environmental uncertainties. Further, embodiments of the present disclosure can facilitate sequencing of a vehicle's faults and/or anomalous operation and/or behavior, identify dependencies between a vehicle's faults and/or anomalous operation and/or behavior, and can detect and diagnose faults and/or anomalous operation and/or behavior in a contextual manner.


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    Title :

    Systems and methods for causal detection and diagnosis of vehicle faults


    Contributors:

    Publication date :

    2024-07-09


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G07C TIME OR ATTENDANCE REGISTERS , Zeit- oder Anwesenheitskontrollgeräte / B64C AEROPLANES , Flugzeuge / B64U / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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