Brenden Harris | Searcharxiv

arXiv2026

Two-stage monitoring design and budgeted condition-based maintenance for LED lighting systems: a gamma-process-based ensemble Kalman filter approach

Light-emitting diode (LED) luminaires degrade gradually during operation, reducing working plane (WP) illuminance and increasing the risk of inadequate indoor lighting. Because individual luminaire degradation states are difficult to measure directly after installation, this paper estimates latent luminaire degradation states from sparse in situ WP illuminance measurements. A two-stage monitoring design and budgeted condition-based maintenance (CBM) optimization framework is proposed. At the design stage, Radiance simulations are used to construct a linear-Gaussian observation model that maps latent luminaire degradation states to WP illuminance measurements. An identifiability-constrained D-optimal design then selects an informative reference layout of WP measurement points for inverse state estimation at a single epoch. At the runtime stage, the measurement design determines the visit schedule and the active reference measurement points for each visit, while an ensemble Kalman filter (EnKF) combines these sparse measurements with nonhomogeneous gamma process degradation predictions to update latent luminaire degradation state estimates. Posterior predictive failure probabilities are used as the risk metric for preventive replacement. Under a monitoring budget, the resulting runtime monitoring and CBM policy jointly selects the visit schedule, active reference measurement points, and risk threshold to minimize expected total downtime and replacement cost. A case study on an office lighting zone demonstrates design-stage measurement layout selection, runtime-stage measurement design, and budgeted CBM policy optimization.

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arXiv2026

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.

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