Gamma Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems
LED lighting systems consist of multiple LED luminaires, each subject to gradual package degradation and abrupt driver failures. Their acceptability is governed by long-term spatio-temporal illuminance compliance on the working plane, which is shaped collectively by all luminaire states, rather than by individual luminaire reliability alone. Current practice, however, verifies compliance only at commissioning through static indices, which cannot capture intermittent or persistent violations accumulated over the operating horizon. This paper proposes a performance-driven, simulation-in-the-loop framework for opportunistic maintenance optimization of LED lighting systems. Package degradation is modeled by a non-homogeneous Gamma process aligned with the IESNA TM-21 lumen maintenance projection method. Driver failures are described by a Weibull lifetime model. The two mechanisms are then integrated into a unified luminaire state via a competing-failure formulation. Model parameters are calibrated from IESNA LM-80 test data via Bayesian inference, with uncertainty propagated from stress levels to service conditions. A programmatic workflow integrates degradation modeling, ray-tracing simulation, and performance evaluation, mapping stochastic luminaire trajectories to the working plane illuminance field. To characterize long-term system performance, a deficiency ratio is defined to quantify the fraction of operational time during which average illuminance or uniformity requirements are violated. To enable scalable Monte Carlo evaluation, a surrogate model with high predictive fidelity replaces repeated Radiance evaluations. The opportunistic maintenance policy is then optimized in a multi-objective setting, balancing performance deficiency and maintenance effort. A realistic office-zone case study demonstrates the framework and reveals Pareto trade-offs for maintenance decision support.