arXiv subjects

Huy Truong-Ba

Publications and source records attributed to Huy Truong-Ba.

6 recordsLinked to original sources

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.

eess.SY↗

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.

eess.SY↗

arXiv2026

Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering

Track geometry monitoring is essential for maintaining the safety and efficiency of railway operations. While Track Recording Cars (TRCs) provide accurate measurements of track geometry indicators, their limited availability and high operational costs restrict frequent monitoring across large rail networks. Recent advancements in on-board sensor systems installed on in-service trains offer a cost-effective alternative by enabling high-frequency, albeit less accurate, data collection. This study proposes a method to enhance the reliability of track geometry predictions by integrating low-accuracy sensor vibration signals with degradation models through a Kalman filter framework. An experimental campaign using a low-cost sensor system mounted on a TRC evaluates the proposed approach. The results demonstrate that incorporating frequent sensor data significantly reduces prediction uncertainty, even when the data is noisy. The study also investigates how the frequency of data recording influences the size of the credible prediction interval, providing guidance on the optimal deployment of on-board sensors for effective track monitoring and maintenance planning.

eess.SY↗

arXiv2026

Coating Breakdown Prediction for Ships and Inspection Planning

Marine corrosion significantly reduces a ship's availability, increases costs of operation and could impact safety. Protective coatings mitigate these risks, but their effectiveness deteriorates over time. Early detection of coating breakdown is crucial to prevent costly repairs and safety concerns. While corrosion itself is well-understood, coating degradation remains under-investigated due to insufficient long-term data. This work addresses this knowledge gap by enhancing coating defect prediction and optimizing inspection planning for ships. The Power Law Non-Homogeneous Poisson Process (PL-NHPP) is utilized for modeling coating defect arrivals. Unlike prior studies, we employ a hierarchical Bayesian approach for parameter fitting, effectively addressing limitations associated with scarce real-world data. Furthermore, we optimize inspection planning by incorporating out-of-service costs and potential costs increases due to delayed repairs. The efficacy of these methods is evaluated through a comprehensive case study involving a recently commissioned fleet with limited historical data. This research contributes to the advancement of condition-based maintenance (CBM) strategies for ships by enabling more accurate prediction of coating breakdowns and optimizing inspection schedules early in the life of the fleet. This approach ultimately improves operational efficiency and reduces life-cycle costs.

stat.AP↗

arXiv2025

Bayesian Multivariate Track Geometry Degradation Modelling and its use in Condition-Based Inspection

Effective maintenance of railway infrastructure is crucial for safe and comfortable transportation. Among the various degradation modes, track geometry deformation due to repeated loading significantly impacts operational safety. Detecting and maintaining acceptable track geometry involves the use of track recording vehicles (TRVs) that inspect and record geometric parameters. This study aims to develop a novel track geometry degradation model that considers multiple indicators and their correlations, accounting for both imperfect manual and mechanized tamping. A multivariate Wiener model is formulated to capture the characteristics of track geometry degradation. To address data limitations, a hierarchical Bayesian approach with Markov Chain Monte Carlo (MCMC) simulation is employed. This research contributes to the analysis of a multivariate predictive model, which considers the correlation between the degradation rates of multiple indicators, providing insights for rail operators and new track-monitoring systems. The model's performance is validated through a real-world case study on a commuter track in Queensland, Australia, using actual data and independent test datasets. Additionally, the study demonstrates the application of the proposed multivariate degradation model in developing a condition-based inspection policy for track geometry, potentially reducing the number of TRV runs while maintaining abnormal detection levels and failure rates.

stat.AP↗

arXiv2024

A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independently of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with solar irradiation and electricity price forecasts as well as limited storage capacity. This study proposes the Stochastic Mixed Integer Linear Program (SMILP) to maximize expected profit within a specified scenario space. The SMILP scenario space is generated by different Empirical Cumulative Distribution Function percentiles of the potential solar energy to accumulate in storage and the expected profit is estimated using the Sample Average Approximation (SAA) method. SMILP exhibits robust performance, however, its computational time poses a challenge. Thus, three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate dispatching plans (DPs). The candidate DP with the best average performance on all profiles is then selected. The new methods were applied to a case study for a 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6% to 9% higher profit than the closest benchmark when the DP is applied to novel weather conditions. With a large historical weather data, the performance of SMILP and Heuristic-2 becomes nearly identical because the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, Heuristic-2 emerges a practical alternative, since it provides similar average profit in a reasonable time (saving about 7 hours in computing time).

eess.SY↗