From reflectometry methods, this work aims at locating accurately electrical faults in complex wiring networks. Increasing demand for online diagnosis has imposed serious challenges on interference mitigation. In particular, diagnosis has to be carried out while the target system is operating. The interference becomes more even critical in the case of complex networks where distributed sensors inject their signals simultaneously. The objective of this paper is to develop a new embedded diagnosis strategy in complex wired networks that would resolve interference problems and eliminate ambiguities related to fault location. To do so, OMTDR (Orthogonal Multi-tone Time Domain Reflectometry) method is used. For better coverage of the network, communication between sensors is integrated using the transmitted part of the OMTDR signal. It enables data control and transmission for fusion to facilitate fault location. In order to overcome degradation of diagnosis reliability and communication quality, we propose a new sensor clustering strategy based on network topology in terms of distance and number of junctions. Based on CAN bus network, we prove that data fusion using sensor clustering strategy permits to improve the diagnosis performance.

The continued operation of aircraft beyond their initially intended service life, combined with the increasing electrification of onboard systems, has intensified the need for reliable diagnosis and monitoring of electrical wiring interconnection systems (EWIS). The latter usually operates in harsh environments, exposed to mechanical, thermal, and electromagnetic stresses that can lead to faults such as insulation damage, conductor breaks, and connector failures. Moreover, since the EWIS is often embedded within the aircraft structure, it presents significant challenges for fault detection and localization, yet failures can have catastrophic consequences. In this context, the early diagnosis and monitoring of transient faults (i.e., arcing) and soft faults becomes mandatory to enable predictive maintenance strategies aimed at enhancing operational safety and reducing unscheduled downtimes and costs. Reflectometry has emerged as a non-intrusive and cost-efficient technique, enabling fault detection and localization through the analysis of signal reflections caused by impedance discontinuities. Advanced variants based on spread spectrum and multi-carrier reflectometry have been developed for real-time monitoring. However, realistic operating conditions impose limitations related to signal attenuation, distortion, and complex topologies. This chapter proposes to study reflectometry-based strategies to overcome challenges imposed by the aircraft environment, such as embedded and distributed diagnostic approaches. These strategies, leveraging multiple sensor nodes and real-time data fusion, represent a key enabling technology for next-generation intelligent EWIS health monitoring systems in safety-critical aerospace environments.