基于MR的机电装备智能检测维修

Intelligent inspection and maintenance of mechanical and electrical equipment based on MR

研究Faster R-CNN目标检测网络的基本结构与训练方法;建立了机电装备状态数据集,训练了目标检测网络,一步实现了指针式仪表区域的提取、数字式仪表读数的识别以及开关、插头状态的识别;在不同视角和光照强度下对目标检测网络进行了测试,结果表明模型在不同的环境中均能保持90%以上的准确度。并以此为依据推理故障的原因,最后根据推理结果,使用基于Unity 3D软件与Hololens 2硬件开发的机电装备智能维修辅助系统来调取混合现实(MR)全息诱导维修信息,以指导保障人员进行操作。实验验证了系统的可用性,实验结果显示使用MR可以快速、高效地完成维修任务。并依据操作耗时和问卷调查进行测试与评价,对系统的优越性进行了定性分析。

This paper examined the basic structure and training method of Faster R-CNN(convolutional neural networks)target detection network.The state data set of mechanical and electrical equipment was established,and the target detection network was trained.In a single step,the region of pointer instrument could be extracted,and the reading of digital instrument and the state of switch and plug could be recognized.The target detection network was tested under different viewing angles and illumination intensities.The results show that the model can maintain the accuracy of more than 90%in different environments.Finally,based on the reasoning results,the intelligent maintenance assistant system for mechanical and electrical equipment developed based on Unity 3D software and HoloLens 2 hardware was applied to the retrieval of the mixed reality(MR)holographic induction maintenance information,thus guiding the operation of the support personnel.In order to verify the availability of the system,the experimental verification process was added,and the experimental results show that the experimenter could complete the maintenance task quickly and efficiently using MR.In addition,test and evaluation were conducted based on the operation time and questionnaire survey,and qualitative analysis was carried out regarding the advantages of the system.