Aviation electrical connectors are essential components in the aircraft electrical wiring interconnection system (EWIS), responsible for information and energy transmission. Even a minor fault in a connector pin can critically affect the reliability and stability of the EWIS. However, traditional faulty pin detection methods rely heavily on manual visual inspection, which is inefficient and susceptible to missed or false detections due to the inherent limitations of human observation. To address these challenges, this article introduces a novel system-level approach that transforms the defect detection process from the physical domain to a virtual one powered by digital twin (DT) technology. A general framework for DT-based defect detection is proposed and instantiated through the design and implementation of a DT-enabled automated faulty pin detection (DT-AFPD) system. The DT-AFPD system integrates 3-D machine vision into a complete detection pipeline encompassing equipment design, data acquisition, DT model construction, algorithm development, and system deployment. Specifically, a 4-degree-of-freedom (4-DOF) device equipped with a 3-D structured light camera is developed to acquire point cloud data of aviation connectors. Several preprocessing techniques are applied to reduce data volume and enhance point cloud quality. Based on this, a connector structure-aware faulty pin detection algorithm, named CSA-FPD, is designed to detect short and bent pins using limited data. The proposed DT-AFPD system is validated on 13 representative types of aviation electrical connectors, covering over 3600 pins. Experimental results demonstrate that the system achieves an average detection precision of 99.85%, effectively reducing the probability of EWIS reinstallation and enhancing the reliability of faulty pin detection.