Advanced uncertainty quantification with dynamic prediction techniques under limited data for industrial maintenance applications.

Engineering systems are expected to function effectively whilst maintaining reliability in service. These systems consist of various equipment units, many of which are maintained on a corrective or time-based basis. Challenges to plan maintenance accounting for turnaround times, equipment availability and resulting costs manifest varying degrees of uncertainty stemming from multiple quantitative and qualitative (compound) sources throughout the in-service life. Under or over-estimating this uncertainty can lead to increased failure rates or, more often, unnecessary maintenance being carried out. As well as the quality availability of data, uncertainty is driven by the influence of expert experience or assumptions and environmental operating conditions. Accommodating for uncertainty requires the determination of key contributors, their influence on interconnected units and how this might change over time. This research aims to develop a modelling approach to quantify, aggregate and forecast uncertainty given by a combination of historic equipment data and heuristic estimates for in-service engineering systems. Research gaps and challenges are identified through a systematic literature review and supported by a series of surveys and interviews with industrial practitioners. These are addressed by the development of two frameworks: (1) quantify and aggregate compound uncertainty, and (2) predict uncertainty under limited data. The two frameworks are brought together to produce the Multistep Compound Dynamic Uncertainty Quantification (MCDUQ) app, developed in MATLAB. Results demonstrate effective measurement of compound uncertainties and their impact on system reliability, along with robust predictions under limited data with an immersive visualisation of dynamic uncertainty. The embedded frameworks are each validated through implementation in two case studies. The app is verified with industrial experts through a series of interviews and virtual demonstrations.