Advanced Uncertainty Quantification Techniques for Maintenance Planning

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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.