Jiajie Fan

Lifetime Estimation of High-Power White LED Using Degradation-Data-Driven Method

High-power white light-emitting diodes (HPWLEDs) have attracted much attention in the lighting market. However, as one of the highly reliable electronic products which may be not likely to fail under the traditional life test or even accelerated life test, HPWLEDs lifetime is difficult to estimate by using traditional reliability assessment techniques. In this paper, the degradation-data-driven method (DDDM), which is based on the general degradation path model, was used to predict the reliability of HPWLED through analyzing the lumen maintenance data collected from the IES LM-80-08 lumen maintenance test standard. The final predicted results showed that much more reliability information (e.g., mean time to failure, confidence interval, reliability function, and so on) and more accurate prediction results could be obtained by DDDM including the approximation method, the analytical method, and the two-stage method compared to the IES TM-21-11 lumen lifetime estimation method. Among all these three methods, the two-stage method produced the highest prediction accuracy.

Physics-of-Failure-Based Prognostics and Health Management for High-Power White Light-Emitting Diode Lighting

Recently, high-power white light-emitting diodes (LEDs) have attracted much attention due to their versatility in applications and to the increasing market demand for them. So great attention has been focused on producing highly reliable LED lighting. How to accurately predict the reliability of LED lighting is emerging as one of the key issues in this field. Physics-of-failure-based prognostics and health management (PoF-based PHM) is an approach that utilizes knowledge of a products life cycle loading and failure mechanisms to design for and assess reliability. In this paper, after analyzing the materials and geometries for high-power white LED lighting at all levels, i.e., chips, packages and systems, failure modes, mechanisms and effects analysis (FMMEA) was used in the PoF-based PHM approach to identify and rank the potential failures emerging from the design process. The second step in this paper was to establish the appropriate PoF-based damage models for identified failure mechanisms that carry a high risk.

Intelligent production control decision support system for flexible assembly lines

In this study, a production control problem on a flexible assembly line (FAL) with flexible operation assignment and variable operative efficiencies is investigated. A mathematical model of the production control problem is formulated with the consideration of the time-constant learning curve to deal with the change of operative efficiency in real-life production. An intelligent production control decision support (PCDS) system is developed, which is composed of a radio frequency identification technology-based data capture system, a PCDS model comprising a bi-level genetic optimization process and a heuristic operation routing rule is developed. Experimental results demonstrated that the proposed PCDS system could implement effective production control decision-making for solving the FAL.

A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting

This paper investigates a flexible assembly line balancing (FALB) problem with work sharing and workstation revisiting. The mathematical model of the problem is presented, and its objective is to meet the desired cycle time of each order and minimize the total idle time of the assembly line. An optimization model is developed to tackle the addressed problem, which involves two parts. A bilevel genetic algorithm with multiparent crossover is proposed to determine the operation assignment to workstations and the task proportion of each shared operation being processed on different workstations. A heuristic operation routing rule is then presented to route the shared operation of each product to an appropriate workstation when it should be processed. Experiments based on industrial data are conducted to validate the proposed optimization model. The experimental results demonstrate the effectiveness of the proposed model to solve the FALB problem.

Genetic optimization of order scheduling with multiple uncertainties

In this paper, the order scheduling problem at the factory level, aiming at scheduling the production processes of each production order to different assembly lines is investigated. Various uncertainties, including uncertain processing time, uncertain orders and uncertain arrival times, are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived firstly by using probability theory. A genetic algorithm, in which the representation with variable length of sub-chromosome is presented, is developed to generate the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.

Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach

Destructive life test is time-consuming and expensive to estimate the LEDs life.TM-21 standard with least-squares regression has weakness in predicting LEDs life.A dynamic recursive method of PF is developed to model the lumen degradation data.An SMC method is proposed to predict RUL distribution with a confidence interval.PF has higher accuracy than TM-21 standard in the LEDs long-term life prediction. Lumen degradation is a common failure mode in LED light sources. Lumen maintenance life, defined as the time when the maintained percentages of the initial light output fall below a failure threshold, is a key characteristic for assessing the reliability of LED light sources. Owing to the long lifetime and high reliability of LED lights sources, it is challenging to estimate the lumen maintenance life for LED light sources using traditional life testing that records failure data. This paper describes a particle filter-based (PF-based) prognostic approach based on both Sequential Monte Carlo (SMC) and Bayesian techniques to predict the lumen maintenance life of LED light sources. The lumen maintenance degradation data collected from an accelerated degradation test was used to demonstrate the prediction algorithm and methodology of the proposed PF approach. Its feasibility and prediction accuracy were then validated and compared with the TM-21 standard method that was created by the Illuminating Engineering Society of North America (IESNA). Finally, a robustness study was also conducted to analyze the initialization of parameters impacting the prediction accuracy and the uncertainties of the proposed PF method. The results show that, compared to the TM-21 method, the PF approach achieves better prediction performance, with an error of less than 5% in predicting the long-term lumen maintenance life of LED light sources.

Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach

High power white light emitting diodes (HPWLEDs), with advantages in terms of luminous efficacy, energy saving, and reliability, have become a popular alternative to conventional luminaires as white light sources. Like other new electronic products, HPWLEDs must also undergo qualification testing before being released to the market. However, most traditional qualification tests, which require all devices under testing to fail, are time-consuming and expensive. Nowadays, as recommended by the Illuminating Engineering Society (IES, IES-TM-21-11), many LED manufacturers use a projecting approach based on short-term collected light output data to predict the future lumen maintenance (or lumen lifetime) of LEDs. However, this projecting approach, which depends on the least-square regression method, generates large prediction errors and uncertainties in real applications. To improve the prediction accuracy, we present in this paper a nonlinear filter-based prognostic approach (the recursive Unscented Kalman Filter) to predict the lumen maintenance of HPWLEDs based on the short-term observed data. The prognostic performance of the proposed approach and the IES-TM-21-11 projecting approach are compared and evaluated with both accuracy- and precision-based metrics.

Prognostics of Chromaticity State for Phosphor-Converted White Light Emitting Diodes Using an Unscented Kalman Filter Approach

Phosphor-converted white light-emitting diodes (pc-white LEDs) utilize a blue LED chip converted by the phosphor to obtain white light emission. Pc-white LEDs have become one of the most popular white LEDs. The reliability concerns of pc-white LEDs involve both lumen maintenance and chromaticity state. However, previous research on the health of LEDs has not taken chromaticity state shift into consideration. Therefore, this paper investigates the chromaticity state shift of pc-white LEDs during an aging test using a data-driven prognostic approach. The chromaticity coordinates (u, v) in the CIE 1976 color space are used to define the states of chromaticity. The Euclidean distance measures between two different chromaticity states represent the chromaticity state shift of LED after aging. A nonlinear dual-exponential model is selected to describe the chromaticity state shift process. Usually, the LED industry used an extrapolating approach to project future states of LED lighting sources, which relies on the nonlinear least square method to fit the obtained data and extrapolates the fitting curve to get the future state. In this paper, a recursive nonlinear filter (an unscented Kalman filter) is used to track the future chromaticity state. The result shows that the unscented Kalman filter approach can improve the prognostic accuracy more compared with the conventional extrapolating approach.

IoT-Based Prognostics and Systems Health Management for Industrial Applications

Prognostics and systems health management (PHM) is an enabling discipline that uses sensors to assess the health of systems, diagnoses anomalous behavior, and predicts the remaining useful performance over the life of the asset. The advent of the Internet of Things (IoT) enables PHM to be applied to all types of assets across all sectors, thereby creating a paradigm shift that is opening up significant new business opportunities. This paper introduces the concepts of PHM and discusses the opportunities provided by the IoT. Developments are illustrated with examples of innovations from manufacturing, consumer products, and infrastructure. From this review, a number of challenges that result from the rapid adoption of IoT-based PHM are identified. These include appropriate analytics, security, IoT platforms, sensor energy harvesting, IoT business models, and licensing approaches.

Intelligent production planning for complex garment manufacturing

Apparel production is characterised by labour-intensive manual operations, frequent style changes, seasonal demand and shortening production lead times. With fierce competition worldwide, many manufacturers are switching their production from mass mode to lean mode to shorten their response time to changes. In a complex mixed mode production environment, it is very important to allocate job orders to suitable production lines so as to ensure the effective utilization of production resources and on-time completion of all job orders. In this paper, planning algorithms are proposed for automatic job allocations based on group technology and genetic algorithms. For genetic algorithms based intelligent planning algorithms, single-run and multiple-run genetic algorithms are suggested. Real production data are used to validate the proposed method. The proposed algorithms has been shown being able to substantially improve planning quality. These planning algorithms are currently used by apparel manufacturers in Hong Kong as part of their routine planning operations.