A PIM-Characteristic-Based Method for Fault Prediction and Diagnosis in RF Cable Assemblies

This article proposes a fault identification method for radio frequency (RF) cable assemblies based on passive nonlinear characteristics. A temperature and degradation-dependent nonlinear resistance model was developed to represent distributed cable faults and localized SMA/N-type connector contact faults. Equivalent circuits with nonlinear and linear components were constructed to simulate realistic degradation. As typical passive intermodulation (PIM) indicators, IM3, IM5, and IM7 show noticeable differences under different fault modes, while the simulated scattering parameters vary only slightly. This indicates that PIM products are more effective for distinguishing fault conditions and are used as the input features for fault identification. Using the equivalent circuit, abundant data from Monte Carlo simulation trained a back-propagation neural network, which determines passive transmission channel fault locations via input IM3/IM5/IM7 values. Experiments were conducted on a passive transmission channel constructed with five cascaded N-type connectors, including different degraded fault cases. The results verified the effectiveness of the proposed method, showing an accuracy of over 90%. These findings demonstrate the effectiveness of combining nonlinear physical modeling and data-driven analysis for reliable passive RF system fault diagnosis and localization.