Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912823952539648 |
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| author | Shi, Haohao Truong-Ba, Huy Cholette, Michael E. Harris, Brenden Montes, Juan Chan, Tommy |
| author_facet | Shi, Haohao Truong-Ba, Huy Cholette, Michael E. Harris, Brenden Montes, Juan Chan, Tommy |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_09380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems Shi, Haohao Truong-Ba, Huy Cholette, Michael E. Harris, Brenden Montes, Juan Chan, Tommy Systems and Control 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. |
| title | Semi-physical Gamma-Process Degradation Modeling and Performance-Driven Opportunistic Maintenance Optimization for LED Lighting Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.09380 |