Composite Hybrid Framework for Through-Life Multi-Objective Failure Analysis and Optimisation

Complex engineering systems include several subsystems that interact in a stochastic and multifaceted manner with multiple failure modes (FMs). The dynamic nature of FMs introduces uncertainties negatively impact the reliability, risk, maintenance complex systems. Traditional approaches adopting standalone techniques for managing independently at various stages asset life cycle pose challenges related to utilisation, costs, availability, some cases, accidents. Therefore, this paper proposes composite hybrid framework comprising four independent models comprehensive through-life management optimisation. first model entails mode, effects, criticality analysis (FMECA) fault tree (FTA) identify critical overall subsystem rates. second analyses caused by using Bayesian discretisation. third adopts Gaussian process regression machine learning technique evaluate wear loss. fourth evaluates risk factorisation elimination method based on causes. Finally, decision-making step is used results previous steps decide an appropriate strategy. proposed verified through case study UK-based train operator's pantograph system. show inspection intervals strategy obtained strike good balance between safety fleet availability.