This paper proposes a fault diagnosis method for external short circuit detection based on the supervised statistical learning. The maximum likelihood estimator is used to capture the statistical properties of the fault and non-fault datasets, and the Gaussian classifier is applied to distinguish the two states. Validation experiments demonstrate the good performance of the proposed method in dynamic conditions. Compared to the prevailing fault detection methods, this method does not require extensive modeling work, determines the fault completely based on the data in existing fault occurrence, and can be adopted easily with the trend of big data and connected vehicles.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    External short circuit fault diagnosis based on supervised statistical learning


    Beteiligte:
    Xia, Bing (Autor:in) / Shang, Yunlong (Autor:in) / Nguyen, Truong (Autor:in) / Mi, Chris (Autor:in)


    Erscheinungsdatum :

    01.08.2017


    Format / Umfang :

    304315 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Semi-supervised Learning Based Intelligent Fault Diagnosis Methods

    Li, Weihua / Zhang, Xiaoli / Yan, Ruqiang | Springer Verlag | 2023


    Supervised SVM Based Intelligent Fault Diagnosis Methods

    Li, Weihua / Zhang, Xiaoli / Yan, Ruqiang | Springer Verlag | 2023




    GAN-DRSN based Inter-turn Short Circuit Fault Diagnosis of PMSM

    Li, Ming / Wang, Manyi / Chen, Longmiao et al. | British Library Conference Proceedings | 2022