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The integrity and functionality of the control and instrumentation (C&amp;I) cable systems are essential when it comes to ensuring the reliability and safety of system operations, especially in vehicles or power plants. Whenever a fault occurs in a multi-core cable, it not only affects signals of the individual faulty line but inflicts the rest through crosstalk and noise interference. Thus, it is imperative that cable diagnostic technologies are eligible of detecting the fault and further differentiating the faulty line to prevent the original fault from jeopardizing the entire system operation. We propose here a diagnostic method which detects the presence and the location of a fault, and further differentiates the faulty line within the multi-core C&amp;I cables using a machine learning algorithm based on the time-frequency domain reflectometry results. Neural networks and the hierarchy clustering algorithm are used for fault detection and the identification of the faulty line. The proposed clustering algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&amp;I cables for nuclear power plants. Hence, the proposed algorithm allows a fault in multi-core cables to be accurately detected and estimated when given the location and the reflection coefficient of a fault.

Fault Detection in Multi-Core C&I Cable via Machine Learning Based Time-Frequency Domain Reflectometry