Predictive Maintenance for Electrical Wiring Faults

Electrical systems in aviation are responsible for the effective and safe operation of essential systems of modern aircraft. Aviation maintenance personnel routinely inspect and maintain these systems to guarantee their integrity and reliability. Currently, aircraft inspections are performed manually by maintenance personnel with a flashlight, a mirror, and a notebook. This manual inspection method comes with its disadvantages, such as high worker hours and dependence on the inspector’s expertise to correctly identify and assess all discrepancies they come across. This is a dataset to support automatic optical inspection tool for electrical components using computer vision. This dataset was built from scratch and hand-labeled. This dataset includes images of a single computer case with multiple configurations of two power supply units, two cooling components, and four SATA cables with several wiring configurations, including various induced faults. Images were collected using a Canon S95 PowerShot camera mounted on a tripod in a windowless room with controlled lighting. Images were labeled in accordance with FAA best practices by a veteran aviation electrician trained in the FAA's EWIS standards using Intel’s CVAT tool. Localization was provided in the images using bounding boxes, allowing the use of existing object detection tools. Class labels were identified based on documented faults, best practices, and the expert opinion of maintenance personnel. Fault labels included misrouted cables/wires, damaged cables/wires, disconnected plugs and disconnected jacks. Faults were intentionally introduced during data collection by misrouting, unplugging, or damaging cables/wires.航空电气系统承担着保障现代航空器关键系统高效、安全运行的重要职责。航空维修人员需定期对这类系统开展巡检与维护，以确保其完整性与可靠性。当前，航空器电气检查仍由维修人员借助手电筒、反光镜与笔记本手动完成。这种人工巡检方式存在诸多弊端，例如工时成本高昂，且高度依赖检查员的专业能力，才能准确识别并评估所有遇到的异常状况。本数据集旨在为基于计算机视觉的电气组件自动光学检测工具提供支撑。 本数据集从零构建并经人工标注。数据集包含单台计算机机箱的多组配置图像，涵盖两台电源单元、两个散热组件以及四根SATA（Serial Advanced Technology Attachment）线缆的多种布线方案，同时包含各类人为引入的故障场景。图像采集使用架设在三脚架上的佳能PowerShot S95相机完成，拍摄环境为一间光照可控的无窗房间。 标注工作由一名经过美国联邦航空管理局（FAA, Federal Aviation Administration）电气线路互联系统（EWIS, Electrical Wiring Interconnection System）标准培训的资深航空电工，严格遵循FAA最佳实践，通过英特尔（Intel）的CVAT（Computer Vision Annotation Tool）标注工具完成。图像中已通过边界框提供目标定位信息，可直接适配现有目标检测工具使用。类别标签的确定依据为已记录的故障类型、行业最佳实践以及维修人员的专家意见。故障标签涵盖线缆布线错误、线缆损坏、插头脱离以及插孔脱离等类型。在数据采集阶段，研究人员通过错误布线、拔插线缆或损坏线缆的方式人为引入各类故障。