Article Dans Une Revue Sustainability Année : 2025

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

Résumé

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.

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hal-05283467 , version 1 (04-05-2026)

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Abdelhak Goudjil, Mostafa Kamel Smail, Mouaaz Nahas. An End-to-End Approach Based on a Bidirectional Long Short-Term Memory Neural Network for Diagnosing Wiring Networks Using Reflectometry. Sustainability, 2025, 17 (14), pp.6241. ⟨10.3390/su17146241⟩. ⟨hal-05283467⟩
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