Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems.

Abboush, Mohammad; Bamal, Daniel; Knieke, Christoph; Rausch, Andreas

Abboush, Mohammad; Bamal, Daniel; Knieke, Christoph; Rausch, Andreas.

Afiliación

Abboush M; Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.
Bamal D; Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.
Knieke C; Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.
Rausch A; Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.

Sensors (Basel) ; 22(11)2022 May 27.

Article en En | MEDLINE | ID: mdl-35684686

Palabras clave

1D-CNN; LSTM; automotive software systems; deep learning; fault detection and classification; hardware-in-the-loop (HIL); model-based testing phases; real-time fault injection

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza

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