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An End-to-End Approach Based on a Bidirectional Long Short-Term Memory Neural Network for Diagnosing Wiring Networks Using Reflectometry

Author

Listed:
  • Abdelhak Goudjil

    (Aerospace Systems Department, Institut Polytechnique des Sciences Avancees (IPSA), 63 Boulevard de Brandebourg, 94200 Ivry-sur-Seine, France)

  • Mostafa Kamel Smail

    (Aerospace Systems Department, Institut Polytechnique des Sciences Avancees (IPSA), 63 Boulevard de Brandebourg, 94200 Ivry-sur-Seine, France
    Group of Electrical Engineering Paris (GeePs), UMR CNRS 8507, CentraleSupelec, Université Paris-Saclay, Sorbonne University, 11 Rue Joliot Curie, 91192 Gif-sur-Yvette, France)

  • Mouaaz Nahas

    (Department of Electrical Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

Abstract

This paper introduces a novel end-to-end fault diagnosis framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Time-Domain Reflectometry (TDR) for the detection, characterization, and localization of wiring faults. The method is designed to operate directly on TDR signals, requiring no manual feature extraction or preprocessing. A forward model is used to simulate TDR responses across various fault scenarios and topologies, serving as the basis for supervised learning. The proposed BiLSTM-based model is trained and validated on common wiring network topologies, demonstrating high diagnostic performance. Experimental results show a diagnostic accuracy of 98.97% and a macro-average sensitivity exceeding 98%, outperforming conventional machine learning techniques. In addition to technical performance, the proposed approach supports sustainable and predictive maintenance strategies by reducing manual inspection efforts and enabling real-time automated diagnostics.

Suggested Citation

  • Abdelhak Goudjil & Mostafa Kamel Smail & Mouaaz Nahas, 2025. "An End-to-End Approach Based on a Bidirectional Long Short-Term Memory Neural Network for Diagnosing Wiring Networks Using Reflectometry," Sustainability, MDPI, vol. 17(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6241-:d:1696873
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    References listed on IDEAS

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    1. Abdelhak Goudjil & Mostafa Kamel Smail, 2025. "Wiring Network Diagnosis Using Reflectometry and Twin Support Vector Machines," Sustainability, MDPI, vol. 17(5), pages 1-17, February.
    2. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
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