Data analytics for risk based decision-making in asset integrity management

结构健康监测系统（SHMS）越来越多地用于评估它们所应用的资产的状况。然而，许多资产运营商在通过SH MS处理大量可用数据时面临挑战，以便获得有关基础资产的有意义的信息和情报。挑战通常是由于许多因素造成的，包括：-处理实时数据所需的计算能力-缺乏基于物理的退化/损坏评估模型或其局限性-缺乏历史记录！数据使机器学习技术能够用于进行诊断或诊断。对于为操作员提供瓦尔的SHMS，它们需要向决策支持工具提供输入，以使操作员能够管理其资产。该博士将专注于原始SHMS数据及其处理，以便为基于风险的决策支持工具提供适当的输入，以管理资产的完整性。将开发算法来处理数据，以便提取诊断或预测信息，以便为决策提供输入。该主题需要多学科的方法，包括无损检测，结构健康评估系统和基于风险的决策工具。在应用统计技术，特别是机器方面的专业知识和经验！earning / artiflcial！开发适当的数据处理方法需要智慧。

Structura l Health Monitoring Systems (SHMSs) are increasingly used to assess the condition of the assets to which they are a pplied. However, many asset operators face chaJ/enges in the processing of vast amount of data that is avalla ble to them through SH MS, in order to get meaningful information and Intelligence a bout the underlying asset.The cha llenge is often d ue to a number of factors in cluding:- Com puting capa bility required to process real-time data- Lack of or limitations of physics-based degradation/ damage assessment models- lack of hlstorica! data to ena ble machine learning techniques to be used to make diagnostic or prognosticFor a SHMS to provide val ue to operators, they need to provide inputs to decision support tools that enable operators to manage their assets. This PhD will focus on raw SHMS data and its processing such that a ppropriate inputs are provided to risk based decision support tools for managing the integrity of assets. Algorith ms will be developed to process data so that diagnostic or predictive information is extracted in order to provide inputs to decision making.The topic requires a multi-disciplinary a pproach with an appreciation of Non-destructive testing, Structural Health Monitoringsystems, and risk based decision-making tools. Specialist knowledge and experience in the a pplication statistical techniq ues particula rly machine !earning / artiflcial !ntelllgence will be required for developing a ppropriate data processi ng methods.