A wireless nondestructive fault detection test for loose or damaged connectors is demonstrated. An architecture known as the conditioned multiclassification of stimulated emissions (CMSE) is pretrained on simulated and empirical radar outputs, and transfer learning is applied to classify connected and disconnected coaxial interconnections. The two main data conditioning methods of this architecture, a statistical signal analysis tool and a convolutional filter bank, are evaluated in order to determine the cost-value proposition of each component. Novel contributions of this technique include the use of two simulation-aided convolutional filter banks to generate a multinetwork ensemble and transfer learning from artificial neural networks trained on two primitive datasets revolving around the electromagnetic phenomena of reflection and filtering. A total of 560 different neural network topologies across four different signal conditioning configurations are considered, with all results compared against the current standard for measurement of cable and connection faults, time-domain reflectometry. Metrics used for comparison are time (training and evaluation), detection (connector engagement at state change detection), and clustering (projection space performance, used as a measure of transfer learning potential). It is determined that the full CMSE architecture performs best, with nearly any neural network topology of this configuration displaying an early detection improvement of 113% and requiring 30% less time to execute an individual classification versus the current standard, all while meeting the most stringent definitions of nondestructive evaluation (NDE).

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