Advanced Reliability, Availability and Maintainability (RAM) Modelling for Critical Energy and Transport Systems

Ensuring the reliability and availability of modern engineering systems is fundamental to the safety, efficiency, and sustainability of future energy and transport infrastructures. This PhD project aims to develop new modelling, analysis, and decision-support methods for reliability, availability and maintainability (RAM) within complex, data-rich engineering environments such as renewable powertrains and energy-supply systems. As digitalisation, electrification, and decarbonisation reshape engineering practice, the reliability challenges faced by industry are rapidly evolving.

In renewable energy and low-carbon transport sectors, failure modes are changing, maintenance windows are increasingly constrained, and system health data - though abundant - are often complex and difficult to interpret. Similarly, maintaining the availability of critical gas-supply and energy-conversion plants requires integrated modelling approaches that capture component degradation, maintenance logistics, and operational flexibility. Traditional reliability models struggle to handle dynamic configurations, interacting subsystems, and health-monitoring feedback, whereas Petri nets and related stochastic modelling formalisms now offer powerful frameworks for representing these dynamic dependencies and maintenance actions within full-system RAM analyses.

This research will investigate new methods to enhance the predictive power and interpretability of RAM models for complex engineered systems. Depending on the candidate’s background and interests, potential research directions include developing Petri net–based frameworks for modelling failure, repair, and maintenance of multi-component systems; integrating health-monitoring data and condition-based maintenance into system-level availability models; conducting reliability and availability assessments for renewable or hybrid powertrains such as wind-hydrogen, battery-electric, or fuel-cell systems; modelling plant capability for gas or energy-supply networks by linking equipment degradation, maintenance strategy, and operational throughput; combining stochastic Petri nets with machine-learning-assisted health prognostics for hybrid physics-based and data-driven modelling; and optimising maintenance schedules and resource allocation to balance reliability, availability, cost, and sustainability. The methodology will involve developing mathematical and computational Petri net models, including stochastic and coloured Petri nets, and using Monte Carlo simulation and reliability block diagram equivalents for model validation.

The project will incorporate sensor-based health indicators and degradation data into dynamic availability models and apply the developed methods to industrial or research case studies in renewable energy systems and gas-supply plants. Collaboration with industrial partners will be sought to provide opportunities to evaluate real-world maintainability and system performance impacts. 94% of Loughborough’s