Risk assessment for linear assets with estimation and model-based uncertainty

基于估计和模型的不确定性的线性资产风险评估

• 批准号: 488544-2015
• 负责人: Kim, Michael
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586293
• 关键词: Risk; assessment; linear; assets; estimation

An asset is anything that has potential value to an organization; asset management is the co-ordinated activity of an organization to realize value from its assets. Riva Modeling is a Toronto-based software company that offers holistic physical asset management solutions that integrate data from a variety of sources and provides data-driven asset management decision support tools. The Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto is the leading industry-academic research group in asset management. Riva seeks to operationalize ongoing research results being made at C-MORE, specifically by making more accurate remaining useful life estimates by accounting for both estimation uncertainty and possible model-misspecification. Our approach is to embed the problem in a Bayesian framework that adds model-misspecification to the equations. C-MORE's methodology is innovative in the way our formulation of the problem can account for model error that is easier and faster to calculate than currently known techniques. Riva Modeling will play a key role in the research by dedicating staff time to the implementation of the theoretical results we will obtain and by connecting us with real industry datasets through its customer base. Riva will also contribute to the computing needs of C-MORE during the development phase of the project.

资产是对组织有潜在价值的任何东西;资产管理是组织的协调活动， 从资产中实现价值。Riva Modeling是一家总部位于多伦多的软件公司， 资产管理解决方案，集成来自各种来源的数据，并提供数据驱动的资产管理决策支持工具。维护优化和可靠性工程中心（C-MORE） 多伦多是资产管理领域领先的行业学术研究团体。 Riva寻求将C-MORE正在进行的研究成果付诸实施，特别是通过使更准确的 通过考虑估计的不确定性和可能的模型设定错误来估计剩余使用寿命。我们 方法是嵌入的贝叶斯框架中的问题，增加了模型的误指定的方程。 C-MORE的方法是创新的，我们对问题的表述可以解释模型误差， 并且比当前已知的技术计算更快。 Riva建模将在研究中发挥关键作用，将员工的时间投入到理论成果的实施中， 将通过其客户群将我们与真实的行业数据集联系起来。里瓦还将为 C-MORE在项目开发阶段的计算需求。