Digital Twin for Predictive Corrosion Management towards Smarter Infrastructures

corsionradar有限公司（CR）和sononomatic有限公司提出了一种用于结构预测腐蚀管理的数字孪生技术。监测和预测腐蚀，特别是绝缘腐蚀（CUI），这可能会导致许多部门的灾难性故障和/或主要停机时间，是项目联盟的主要关注点。CR技术致力于成为智能基础设施遥感技术和先进分析系统的全球领导者。目前，大多数腐蚀检测方法使用的是反应性方法、人工非破坏性技术和基于非常有限数据的风险管理。腐蚀雷达公司开发了一套IIOT（工业物联网）传感器，可以检测和定位绝缘下的水分和腐蚀。该提案旨在扩展传感器的功能，以包括温度测量。将为CUI管理创建该结构的预测性数字孪生。具有机器学习能力的预测模型将通过结合现场CUI数据的知识库，以及来自广泛实验室测试的分析和数据库。数字孪生模型使资产管理公司能够可视化结构的状态，并对未来的腐蚀轨迹进行分析。该项目的成功将为数据驱动的预测性维护铺平道路，在不影响人员、资产和环境安全的情况下降低维护成本。这也将减少停机时间，同样可以应用于许多行业，如石油和天然气，可再生能源，食品加工装置，化学和火力发电厂以及核能。

CorrosionRADAR Ltd (CR) and Sonomatic Ltd proposes a digital twin technology for predictive corrosion management of structures. Monitoring and predicting the corrosion and, specially, Corrosion Under Insulation (CUI), which can potentially cause catastrophic failures and/or major downtime for many sectors, are the main focus of the project consortium. CR technology strives to be a global leader in remote sensing technologies and advanced analytics systems for smart infrastructures. Most of the current practices of corrosion detection use a reactive approach, manual non-destructive techniques and risk management based on very limited data. CorrosionRADAR has developed a suite of IIOT (Industrial Internet of Things) enabled sensors which could detect and locate both moisture and corrosion under insulation. This proposal aims to extend sensor capability to include temperature measurement. A predictive digital twin of the structure will be created for CUI management. Prediction model, with machine learning capability, will be through a combination of knowledge base of field CUI data, analytical and data base from extensive laboratory testing. Digital twin allows the asset manager to visualise the state of structure and carry out what if analysis for future corrosion trajectory. Success of this project will pave the way for data driven predictive maintenance, reducing maintenance cost without compromising on the safety of people, assets and environment. This will also lead to reducing downtime and can equally be applied to many sectors such as oil & gas, renewables, food processing units, chemical and thermal power plants and nuclear.