LED Reliability Assessment Using a Novel Monte Carlo-Based Algorithm

Enayati, Javad; Rahimnejad, Abolfazl; Gadsden, S. Andrew

doi:10.1109/tdmr.2021.3095244

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Investigating the remaining useful life (RUL) of airport ground lighting (AGL), Ruknudeen and Asokan (2017) employed PF and onboard diagnostics to determine L70. Enayati et al (2021) devised a probability density function (PDF) for LED life prediction using the Monte Carlo algorithm (MC) and Nonlinear Kalman filter (IEKF). However, this approach requires advance estimating (preliminary assumptions), which proves counterproductive for identifying new products, and involves a significant amount of computation, making it impractical for online real-time monitoring.…”

Section: Filtering Networkmentioning

confidence: 99%

Overview of high-power LED life prediction algorithms

Sun,

Bai,

Zhang

2024

Front. Sustain. Energy Policy

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Life prediction significantly influences the reliability of LED light sources. While high-power LED light sources theoretically offer a lifespan of up to 100,000 h, irreversible damage to components leads to light failure, substantially reducing their actual lifespan. Consequently, accurate life prediction is pivotal for manufacturers to cut costs and enhance economic efficiency. This necessity aligns with the interests of communities, governments, and consumers. Currently, the most extensively employed prediction methods are based on traditional physical models and data-driven approaches. The focal point of current research lies in realizing model fusion, presenting both a hotspot and a challenge. To elucidate the relationships, advantages, and disadvantages of different algorithms and establish the groundwork for LED life prediction algorithm development, this paper first introduces material properties and the light decay model of high-power LED light sources. Subsequently, it discusses the principles and methods of the physical model concerning light source reliability. The paper also presents a review and comparison of recent domestic and foreign light source life prediction models. Finally, it provides insights into the expected future development trends in life prediction.

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Section: Filtering Networkmentioning

confidence: 99%

Overview of high-power LED life prediction algorithms

Sun,

Bai,

Zhang

2024

Front. Sustain. Energy Policy

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“…However, as the number of iterations increases to achieve better accuracy, the computational complexity introduced by the iterative strategy also increases. Note that for most real-world problems a noticeable improvement in accuracy is obtained after only a few numbers of iterations (in most cases one or two iterations) [29].…”

Section: B Iterated Silfmentioning

confidence: 99%

“…3) Integration of the iterated filtering algorithm [27]- [29] to enhance the accuracy of SILF. The iterative nature of this algorithm refines the estimation results and improves their overall accuracy.…”

mentioning

confidence: 99%

Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy

Rahimnejad

Enayati²,

Vanfretti

et al. 2023

IEEE Open J. Signal Process.

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This paper introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of the Lattice Kalman Filter (LKF) that leverages sliding mode theory. SILF incorporates a sliding boundary layer in the measurement update formulation, enabling the filter innovation to slide within predefined upper and lower bounds. This enhances the robustness of SILF, making it resilient to model uncertainties and noise. Additionally, a derivative-free formulation of SILF is developed using statistical linear regression, eliminating the need for Jacobian calculations. To further improve accuracy, robustness, and convergence behavior in the presence of abrupt changes in system model/parameters, SILF is reinforced with the Iterated Sigma Point Filtering and Strong Tracking Filtering strategies, resulting in the Reinforced Lattice Kalman Filter (RLKF). The experimental findings for the estimation of distorted power waveforms illustrate the superior performance of SILF and RLKF over competing methods, especially when operating in scenarios characterized by model uncertainties and noisy environments.

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“…The estimation paradigms are applied to generate the optimal tracks [25,26]. Methods in the literature mostly apply various forms of the Kalman Filter (KF) which is a fundamental tool for analyzing and solving a broad class of estimation problems [27]. In this work, the tracker estimates the state and state error for each track using an EKF algorithm.…”

Section: Multi Object Tracking (Mot)mentioning

confidence: 99%

A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles

Enayati¹,

Asef²,

Jonnalagadda³

2022

Preprint

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The main functions of the automated systems rely on the advanced sensors for detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect the potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in the extreme weather conditions such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, a triple radar arrangement (long-range, medium-range, and short-range radars) based on sensor fusion technique is proposed to detect objects with different size in level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including truck, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.

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LED Reliability Assessment Using a Novel Monte Carlo-Based Algorithm

Cited by 6 publications

References 25 publications

Overview of high-power LED life prediction algorithms

Overview of high-power LED life prediction algorithms

Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy

A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles

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