Digital Twin for Predictive Corrosion Management towards Smarter Infrastructures

CorrosionRADAR Ltd（CR）和Sonomatic Ltd提出了一种用于预测结构腐蚀管理的数字孪生技术。监测和预测腐蚀，特别是绝缘层下腐蚀（CUI），这可能会导致许多行业的灾难性故障和/或重大停机时间，是项目财团的主要重点。CR科技致力于成为智能基础设施遥感技术和高级分析系统的全球领导者。目前大多数的腐蚀检测实践都是基于非常有限的数据，采用被动的方法、人工非破坏性技术和风险管理。CorrosionRADAR开发了一套支持IIOT（工业物联网）的传感器，可以检测和定位绝缘层下的水分和腐蚀。该提案旨在扩展传感器能力，以包括温度测量。将为CUI管理层创建该结构的预测数字孪生模型。预测模型，具有机器学习能力，将通过结合实地CUI数据的知识库，分析和数据库从广泛的实验室测试。数字孪生使资产管理者能够可视化结构状态，并对未来的腐蚀轨迹进行假设分析。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。这也将导致减少停机时间，并同样适用于许多行业，如石油和天然气，可再生能源，食品加工单位，化工和热电厂以及核电厂。

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.