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

TL;DR

The paper addresses long-term maintenance of large-scale LED lighting systems by jointly modeling gradual LED-package degradation with abrupt driver outages, and by evaluating maintenance policies against a dynamic, system-level lighting service metric. It advances a semi-physical Gamma-process degradation model, Bayesian calibration from LM-80 data, and a physics-based Radiance mapping to obtain a dynamic deficiency ratio that quantifies compliance over time. A surrogate-based performance mapping accelerates Monte Carlo evaluation, enabling Pareto optimization of opportunistic maintenance policies that balance deficiencies, site visits, and replacements. The case study demonstrates actionable Pareto fronts for policy selection and highlights the framework's robustness to driver reliability assumptions, with practical implications for maintenance planning and inventory management in real facilities.

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.

Figures (17)

• Figure 1: An LED lighting system, working plane, and calculation grid
• Figure 2: Workflow of Radiance-based working-plane illuminance evaluation
• Figure 3: Illustration of working-plane illuminance fluctuations over operation time
• Figure 4: Static performance indices and standard requirements over time
• Figure 5: The schematic diagram of OM policy