Advanced Uncertainty Quantification Techniques for Maintenance Planning

现代复杂工程系统（CES）期望在保持服务可靠性的同时有效运行。这对自信和准确地预测维护成本和资产可用性提出了重大挑战。这些挑战带来了不同程度的不确定性，这些不确定性来自于多个启发式和统计来源，贯穿CES的整个使用寿命。从统计来源量化不确定性的技术有很好的文献记载，但是获取和分析启发式属性的方法经常是不明确的和未减轻的，这增加了进一步的不确定性。整体的观点对于提高决策能力和减少维护成本和周转时间是必要的。该项目旨在开发一种智能不确定性量化系统，该系统可以从历史设备数据和启发式估计的组合中学习，从而使用户能够预测CES在使用阶段的不确定性水平。这项工作建立在与克兰菲尔德大学全寿命工程服务（TES）中心共同开展的项目的基础上，这些项目已应用于工业领域，以解决招标阶段的成本不确定性问题，这些问题面临着与服务中类似的挑战。影响不确定性和阻碍可靠预测的核心因素包括现有数据、经验和知识的质量。通过与BAE系统公司的专家合作，以及当前不确定性评估和未来海事支持计划的实践来应对挑战。准确的服务预测依赖于可靠的数据和可预测的维护人员性能水平。整体的观点最终允许更成功的决策，但需要在资产生命周期的质量和成本之间进行权衡。

Modern complex engineering systems (CES) are expected to function effectively whilst maintaining reliability in service. This presents significant challenges to confidently and accurately predict maintenance costs and asset availability. These challenges raise varying degrees of uncertainty stemming from multiple heuristic and statistical sources throughout the in-service life of CES. Techniques to quantify uncertainty from statistical sources are well documented, but methods to obtain and analyse heuristic attributes often go undefined and unmitigated, which raise further uncertainties.A holistic view is necessary to improve decision-making capabilities and reduce maintenance costs and turnaround time. This project aims to develop an intelligent uncertainty quantification system that learns from a combination of historic equipment data and heuristic estimates to allow the user to forecast the level of uncertainty through the in-service phase of CES.This work builds on projects undertaken in conjunction with the Through-Life Engineering Services (TES) centre at Cranfield University that have been applied in industry to tackle cost uncertainty at the bidding stage, which face broadly similar challenges as those in service. Core factors that influence uncertainty and hinder confident forecasting include quality of available data, experience and knowledge. These have been examined through collaboration with experts from BAE Systems, along with current practice in uncertainty assessment and future maritime support programmes to address challenges. Accurate forecasts in service depend on reliable data and predictable maintainer performance levels. A holistic view ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset's life cycle.