Using Augmented Intelligence to Automate Subsea Inspection Data Acquisition, Processing, Analysis, Reporting and Access

H. Ferguson, M. D. Gordon, A. Cameron
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Abstract

Augmented Intelligence (AI2) involves fusing Analyst Intuition with Artificial Intelligence to deliver an optimised combination of human-machine decision support. AI2 is being incorporated by i-Tech Services / Leidos into the physical inspection of offshore Oil, Gas, and Renewables assets, delivering valuable data driven insights that contribute to greater efficiency, enhanced condition monitoring, improved asset integrity and asset life extension. The deployment of vehicular and diver assets to obtain such inspection data, with associated support vessels, remains a major cost challenge for Operators. We believe the industry needs to approach this challenge from two key directions. Firstly, through the application of autonomous systems for data acquisition and delivery, reducing vessel reliance, and secondly through automating the acquisition and processing of data and maximising the insight provided by the data. This paper will examine the use of Augmented Intelligence to optimise the Subsea Inspection data workflow as a key use case, to demonstrate the principles. The historic paradigm consists of a fragmented evolving approach, with insufficient consideration and design across all the sensors, processing analytical engines and data visualisation. The approach being adopted is to closely link all aspects of the data workflow, within the context of delivering the data and beyond in terms of harvesting additional insight and value. To achieve the optimum workflow a number of developmental initiatives are being knitted into a modular platform, each element providing standalone value but the sum of the parts generates the most significant value and cost reduction. The elements being combined are automatic data quality control at acquisition source and through the full workflow, automated processing, machine vision for object recognition and reporting and machine learning to optimise the system intelligence. All of these are designed to augment the expertise of the analyst / user, detecting change to learnt parameters, by using real time data and critically by referencing large historical data sets and as-built data. The outputs from a system holistic approach will be improved data acquisition with more efficient high quality right first time data reporting. In addition layers of analytics, with smart, intuitive data access and retrieval will optimise delivery of key information within large data sets, together with maximising value and insight.
使用增强智能自动化海底检测数据采集、处理、分析、报告和访问
增强智能(AI2)涉及将分析师直觉与人工智能相融合,以提供人机决策支持的优化组合。i-Tech Services / Leidos将AI2整合到海上石油、天然气和可再生能源资产的物理检测中,提供有价值的数据驱动见解,有助于提高效率、增强状态监测、改善资产完整性和延长资产寿命。部署车辆和潜水员资产来获取此类检查数据,以及相关的支持船,仍然是运营商面临的主要成本挑战。我们认为,该行业需要从两个关键方向应对这一挑战。首先,通过应用自主系统进行数据采集和交付,减少对船舶的依赖;其次,通过自动化数据采集和处理,最大限度地提高数据提供的洞察力。本文将作为一个关键用例,研究增强智能的使用,以优化海底检测数据工作流程,并演示其原理。历史上的范式是由一种碎片化的进化方法组成的,没有充分考虑和设计所有的传感器、处理分析引擎和数据可视化。所采用的方法是将数据工作流的所有方面紧密联系起来,在交付数据的上下文中,以及在收获额外的见解和价值方面。为了实现最佳的工作流程,许多开发计划被编织到一个模块化平台中,每个元素提供独立的价值,但各部分的总和产生最显著的价值和成本降低。这些元素结合在一起是采集源的自动数据质量控制,通过完整的工作流程,自动处理,用于对象识别和报告的机器视觉以及用于优化系统智能的机器学习。所有这些都是为了增强分析师/用户的专业知识,通过使用实时数据和参考大型历史数据集和已构建数据来检测学习参数的变化。系统整体方法的产出将是改进数据采集,更有效、高质量的首次数据报告。此外,通过智能、直观的数据访问和检索,分析层将优化大型数据集中关键信息的传递,同时实现价值和洞察力的最大化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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