David McMillan is a researcher specialising in reliability engineering, condition monitoring and risk-informed operation and maintenance of wind energy systems. His work is defined by the rigorous application of probabilistic modelling and stochastic simulation to support engineering decision-making under uncertainty.

A central contribution of his research is the development of probabilistic asset models that represent component degradation, failure and repair processes over time. These models typically use discrete-time Markov chains coupled with Monte Carlo simulation to capture uncertainty in component condition, maintenance actions and operational constraints.

His work integrates multiple layers of the system within a single modelling framework, including component-level failure behaviour, wind resource variability, maintenance logistics and economic performance. This enables quantitative comparison of periodic and condition-based maintenance strategies using metrics such as availability, energy yield and lifecycle cost.

A distinctive aspect of David’s research is the explicit treatment of uncertainty associated with condition monitoring systems themselves. Diagnostic accuracy, false positives and missed detections are modelled directly, allowing the economic value of monitoring systems to be assessed realistically rather than through idealised assumptions.

This work has been particularly influential for offshore wind applications, where access limitations, long repair times and high component costs significantly increase the value of well-designed condition-based maintenance strategies.

---

Key expertise

• Probabilistic modelling of wind turbine degradation, failure and repair
• Markov chain and Monte Carlo simulation for asset management
• Quantification of condition monitoring benefit under uncertainty
• Risk-informed optimisation of operation and maintenance strategies
• Decision-support tools for wind farm reliability and lifecycle assessment

David McMillan’s work underpins WECC’s capability in reliability-led asset management, enabling robust, data-informed decisions for the operation and maintenance of wind energy systems operating in uncertain and high-risk environments.