dorsal/arxiv
View SchemaSemi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems
| Authors | Haohao Shi, Huy Truong-Ba, Michael E. Cholette, Brenden Harris, Juan Montes, Tommy Chan |
|---|---|
| Categories | |
| ArXiv ID | 2601.09380vv1 |
| URL | https://arxiv.org/abs/2601.09380 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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"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.",
"arxiv_id": "2601.09380",
"authors": [
"Haohao Shi",
"Huy Truong-Ba",
"Michael E. Cholette",
"Brenden Harris",
"Juan Montes",
"Tommy Chan"
],
"categories": [
"eess.SY",
"cs.SY"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems",
"url": "https://arxiv.org/abs/2601.09380",
"version": "v1"
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