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

Abdelhak Goudjil (), Mostafa Kamel Smail and Mouaaz Nahas
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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
Mouaaz Nahas: Department of Electrical Engineering, Umm Al-Qura University, Makkah 21955, Saudi Arabia

Sustainability, 2025, vol. 17, issue 14, 1-20

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.

Keywords: BiLSTM; reflectometry; diagnosis; wiring network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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