Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems

TL;DR

This paper introduces a simulation-based framework for optimizing maintenance of large LED lighting systems, modeling degradation with semi-physical processes and evaluating performance through illuminance mapping to improve reliability and efficiency.

Contribution

It develops a novel semi-physical degradation model calibrated with Bayesian inference and integrates performance-driven metrics for maintenance decision-making in LED systems.

Findings

Effective degradation modeling using Gamma and Weibull processes.

Surrogate models enable scalable Monte Carlo evaluations.

Optimized maintenance policies improve system performance and reduce costs.

Abstract

Large-scale LED lighting systems degrade through gradual package degradation and abrupt driver outages, while acceptability is determined by spatio-temporal illuminance compliance rather than component reliability alone. This paper proposes a performance-driven, simulation-in-the-loop framework for opportunistic maintenance optimization of LED lighting systems. LED package degradation is modeled by a semi-physical non-homogeneous Gamma process whose mean follows an exponential lumen-maintenance trend, and driver outages are described by a Weibull lifetime model. Parameters are calibrated from LM-80 accelerated degradation data via Bayesian inference, enabling uncertainty propagation to operating conditions. System performance is evaluated using ray-tracing-based illuminance mapping, and static indices (average illuminance and uniformity) are converted into a long-term dynamic deficiency-ratio metric via performance-deficiency durations over event intervals. To enable scalable Monte Carlo policy evaluation and search, a surrogate-based performance mapping replaces repeated ray-tracing with negligible loss of fidelity. An opportunistic policy is optimized in a multi-objective setting to balance performance deficiency, site visits, and replacements. A case study demonstrates the practicality of the framework and the resulting Pareto trade-offs for maintenance decision support.