In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching–learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.

Time-reversal multiple signal classification (TR-MUSIC) is here applied to testing cable networks in order to detect and locate soft faults. TR-MUSIC is shown to provide spatial resolution in the millimeter range while using continuous-wave (CW) test signals, even at frequencies with guided wavelengths much larger than cables length. State-of-the-art time-domain reflectometry (TDR) methods would require bandwidths in the order of hundreds of megahertz for a similar performance. As opposed to TDR, TR-MUSIC does not suffer from the ambiguity created by the existence of multiple echoes in cable networks, which can be easily misinterpreted as multiple faults, leading to false alarms. TR-MUSIC is intrinsically adapted to dealing with multiple faults, handing a direct estimate of the number of faults found in a network under test. Furthermore, the detection capabilities of TR-MUSIC are insensitive to the severity of a fault, as faults are not detected based on the intensity of their echoes as done in TDR techniques, but on a subspace approach mostly dependent on phase patterns. Accurate identification of faults from CW signals points to the possibility of designing simpler test systems, not requiring pulse generators and fast electronics. TR-MUSIC accuracy is demonstrated experimentally for locating both single as well as multiple soft faults in two cable networks. The proposed method also gives access to the reflection coefficient of each fault, thus enabling an estimate of its severity.

Decomposition of the time-reversal (TR) operator (DORT), a recently applied TR method to transmission lines, has proven to be effective in detecting and locating soft faults in complex wire networks. In this paper, we will propose a fault location criterion which will form a later basis for a statistical study investigating the influence of several parameters, namely, the number of testing ports and the position of the fault, on the performance of DORT technique. Notably, this would allow a closer inspection of the method's practicability for future implementation in real-life networks.

Despite the fact that wireless systems are overwhelming most of nowadays applications, wiring networks are still forming a pivotal anchor in many controls and security units. Thus, ensuring the reliable operation of such networks necessitates investing in techniques dedicated for their protection. A vast majority of such methods rely on the presence of a reference model of the network. In this paper, we propose a nondestructive testing approach based on the tenets of reflectometry methods and genetic algorithms to retrieve the topology and load impedances of unknown embedded complex wiring networks.

In the era of the Internet of Things (IoT), electrical wiring interconnection systems (EWIS) are massively hosted everywhere in many fields where the transfer of energy and information is a fundamental pillar to guarantee the good performance of a system. In addition, the huge technological and industrial uprising ensured by the emerging fourth Industrial Revolution is forcing electrical machines and instruments toward massive transformations and more complexity than ever before. Accordingly, the health and integrity of any system are essential for the proper operation of any application. Yet, faults and failures are inevitable. They can lead to catastrophic problems on both economical and human levels. Airplane crashes, massive fires, explosions, and so on are examples of such consequences. Accordingly, fault detection, diagnosis, and location have been always substantially important for ensuring safety, security, integrity, and optimal performance for any system. Notably, fault diagnosis of structural cables integrated in dams, bridges, concrete, and so on have also formed a cornerstone in the context of health monitoring of such structures. The same applies for oil and gas pipelines, subsea cables, umbilicals, and so on.