Predictive maintenance scheduling of recoverable stochastic hybrid systems under degradation and shocks: A case study of an offshore wind farm

• Extend the SHS model with recoverability and derive analytical solutions. • Derive a Markov control policy to maximize long-term asset value. • Infer failure mechanisms to support PdM under stochastic conditions. • Validate the PdM plan on a simulated 16-turbine offshore wind farm. Predictive maintenance (PdM) is recognized as a next-generation maintenance paradigm across industrial applications. However, implementing PdM in complex engineering systems, such as an offshore wind farm, remains highly challenging. These systems are exposed to continuous degradation and external shocks simultaneously and also feature recoverability. Meanwhile, they often exhibit failure interactions, such as common cause failures (CCFs) and dependent or competing failures. These characteristics hinder accurate condition assessment and complicate maintenance planning. To address these challenges, this study extends the stochastic hybrid system (SHS) framework to model the reliability of repairable systems with self-recovery capability under degradation and shock-induced CCFs. Furthermore, a PdM scheduling approach is proposed based on a Markov control policy that maximizes the expected asset value and determines optimal health levels under stochastic market conditions. A case study is conducted on a simulated offshore wind farm with 16 turbines exposed to lethal and non-lethal shocks. Partially observed data from simulations are used to estimate a transition matrix and predict system states for PdM decisions. The objective function minimizes the discrepancy between the estimated Markov control policy and turbine conditions through maintenance actions. The results show that the predicted optimal maintenance plan closely matches the theoretical optimal plan.