新兴的检查技术——实现远程调查/检查

Feng Wen, J. Pray, K. McSweeney, Hai Gu
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引用次数: 7

摘要

新兴的检测技术、工具和平台,如无人机(uav)、远程操作车辆(rov)、机器人爬行器和可穿戴/手持设备,正在创建可操作的数据,以帮助在调查和检测相关活动中做出更明智的决策,并提高流程效率。本文将讨论ABS的举措,以进一步理解和帮助定义这些工具和技术的使用和集成,以支持海运业向数字化转型的发展。ABS与技术设备制造商和服务提供商合作,一直在进行可行性试验,以评估这些技术的实际应用和实施,以支持船级调查。这些试验的重点是使用高清光学设备检测涂层故障,以辅助近距离视觉检查(CVI),并利用移动平台(可穿戴设备和手持设备)与协作软件平台相结合,以虚拟实时(连接)或近实时(断开)的方式执行调查活动,根据类别规则要求捕获数据。为了支持这些试验,ABS积极参与与学术界的联合开发项目(JDP),重点是将图像识别(人工智能[AI])实现到调查决策过程中。作为JDP的一部分,开发了一种人工智能软件,其中包含数千张受损结构涂层图像。这些图像被用于软件图像识别能力的训练、测试和评估。本文讨论了可行性试验的结果以及船级社活动数字化演进的下一步。潜在的应用包括但不限于:基于状态/远程调查,维护计划评估,3D扫描/图像数据捕获的3D模型开发,文档审计和钢板腐蚀测绘。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emerging Inspection Technologies – Enabling Remote Surveys/Inspections
Emerging inspection technologies, tools and platforms such as unmanned aerial vehicles (UAVs), remotely operated vehicles (ROVs), robotic crawlers, and wearable/handheld devices are creating actionable data to help enable more informed decision making and improve process efficiency during survey and inspection related activities. This paper will discuss ABS’ initiatives to further understand and help define the use of and the integration of these tools and technologies to support the evolution of the maritime industry's transition to digitalization. ABS, in conjunction with technology equipment manufacturers and service providers, has been conducting feasibility trials to evaluate the pragmatic application and implementation of these technologies to support Class surveys. These trials have focused on areas such as the detection of coating breakdowns using high-definition optics to aid in close-up visual inspections (CVI) and leveraging mobile platforms (wearable and handheld devices) in conjunction with a collaborative software platform to execute survey activities virtually in real-time (connected) or near real-time (disconnected), capturing data as required by Class Rules. In support of these trials, ABS is actively involved in a joint development project (JDP) with academia focusing on the realization of image recognition (artificial Intelligence [AI]) into the survey decision-making process. As part of this JDP, an AI software was developed incorporating thousands of damaged structural coating images. These images were used for the training, testing and evaluation of the software's image recognition capabilities. This paper discusses the results of the feasibility trials and the next steps in the digital evolution for Classification Society activities. Potential applications include but are not limited to: condition-based/remote surveys, evaluation of maintenance programs, development of 3D models with 3D scanning/image data capture, documentation auditing, and corrosion mapping of steel plates.
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