This paper proposes a load monitoring algorithm that exploits the observed time frequency characteristics of a laboratory pulsed load current and uses it to detect events and characterize them into desirable transitions or faults. An electromagnetic gun is assembled at a low voltage lab setup to provide multiple iterations of pulsed load events with a few instances of faults. Detailed analysis of the load profile is followed by a simulation using measured data to demonstrate the effectiveness of the Short Time Fourier Transform based algorithm to identify key events in the current profile and detect faults.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    STFT-Based Event Detection and Classification for a DC Pulsed Load


    Beteiligte:
    Maqsood, Atif (Autor:in) / Rossi, Nick (Autor:in) / Ma, Yue (Autor:in) / Corzine, Keith (Autor:in) / Parsa, Leila (Autor:in) / Oslebo, Damian (Autor:in)


    Erscheinungsdatum :

    01.08.2019


    Format / Umfang :

    2897953 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Novel Classification Method for Flutter Signals Based on the CNN and STFT

    Shiqiang Duan / Hua Zheng / Junhao Liu | DOAJ | 2019

    Freier Zugriff

    The STFT-Based Estimator of Micro-Doppler Parameters

    Djurovie, Igor / Popovic-Bugarin, Vesna / Simeunovic, Marko | IEEE | 2017


    Measurement based FHSS–type Drone Controller Detection at 2.4GHz: An STFT Approach

    Kaplan, Batuhan / Kahraman, Ibrahim / Gorcin, Ali et al. | IEEE | 2020


    A Variable Speed Fault Detection Approach for Electric Motors in EV Applications based on STFT and RegNet

    Mohammad-Alikhani, Arta / Pradhan, Subarni / Dhale, Sumedh et al. | IEEE | 2023