China
Africa
Explore chapters and articles related to this topic
Evaluating Lighting Equipment
Published in Craig DiLouie, Lighting Redesign for Existing Buildings, 2020
Like conventional sources, LED devices have a declining rate of lumen depreciation over their life. In fact, being an electronic device means that failure modes are quite different from standard lamps, which burn out long before their decline in light output becomes a significant issue. LEDs may last as long as 100,000 hours before failure, but at some point the amount of light being produced is so low that replacement is necessary to restore light levels. Because of this, service life of an LED product is rated at the time in hours of operation where the product is producing 70 percent lumen maintenance (30 percent loss of light output, about 15,000-50,000 hours for an LED device depending on whether the LEDs are overdriven and by how much) for general lighting applications and 50 percent lumen maintenance for decorative and other non-critical applications (up to 50,000-70,000 hours in well-designed products is possible).
Visible-Light-Emitting Diodes (VLEDs)
Published in Dave Birtalan, William Nunley, Optoelectronics, 2018
In LEDs, light output decreases over time, and LEDs rarely suffer from catastrophic failures. A catastrophic failure in LEDs could be a parameter change in electrical or optical specifications that prevents the LED from lighting. As a result, the term lumen maintenance, describing the light output in lumens over time, is used as a metric to describe LED lifetime. According to the Alliance for Solid-State Illumination Systems (ASSIST), two coordinates can be used to describe the lifetime of an LED: L70 and L50. L70 is the time taken to get to the point at which the human eye can detect a noticeable reduction in light levels, which is at 70% lumen maintenance, or a 30% reduction in total lumen output of the LED. Likewise, L50 is the time taken to get to 50% lumen maintenance.
Financing: The key to Africa and the Middle East’s Solar Energy Future
Published in Peter F. Varadi, Frank Wouters, Allan R. Hoffman, Wolfgang Palz, Anil Cabraal, Richenda Van Leeuwen, The Sun is Rising in Africa and the Middle East, 2018
Peter F. Varadi, Frank Wouters, Allan R. Hoffman, Wolfgang Palz, Anil Cabraal, Richenda Van Leeuwen
Lighting Global92 is the World Bank Group’s platform supporting sustainable growth of the off-grid solar market. Through Lighting Global, the International Finance Corporation (IFC) and the World Bank work with the Global Off-Grid Lighting Association (GOGLA), manufacturers, distributors, and other development partners and end users to develop the off-grid lighting market. An important aspect is the quality of the products. Lighting Global provides a quality assurance methodology with the following key aspects:Truth in Advertising: Advertising and marketing materials accurately reflect the tested product performance.Durability: The product is appropriately protected from water exposure and physical ingress and survives being dropped.System Quality: The product passes a visual wiring and assembly inspection.Lumen Maintenance: The product maintains at least 85% of initial light output after 2,000 hours of operation.Warranty: A 1-year (or longer) retail warranty is available.
Interval estimation for Wiener processes based on accelerated degradation test data
Published in IISE Transactions, 2018
Lanqing Hong, Zhi-Sheng Ye, Josephine Kartika Sari
In this section, degradation data for a commercial white LED from a constant stress ADT are analyzed. For LEDs, degradation of the lumen maintenance is generally considered as the main failure mechanism (Meneghini et al., 2010). It is a common practice to define the lifetime of an LED as the time when the decrease in lumen maintenance first crosses the threshold line at of its initial level. In industry practice, the degradation failure threshold is usually set as H = 30, and the corresponding lifetime is deemed L70.
Strategic allocation of test units in an accelerated degradation test plan
Published in Journal of Quality Technology, 2019
Zhi-Sheng Ye, Qingpei Hu, Dan Yu
The study may be extended in several important directions. The first is to apply the strategy to stochastic process models, such as the Wiener process (Zhai and Ye 2017) and the gamma process. It is straightforward to extend (1) to a Wiener process with random-effects as , where is bivariate normal, σ is the volatility parameter, and is the standard Brownian motion. In the model, Y(t) has a closed-form PDF. However, extension of (1) to the gamma process is difficult, as it is hard to find a bivariate distribution to describe the initial degradation and the degradation rate under the gamma process model. For this process, the unknown-onset model (10) is useful as it links the initial degradation and the degradation rate through the onset parameter τ. This indicates the potential applications of the unknown-onset model in degradation analysis. The second direction is the model optimization. Because there are only two planning variables in our ADT setting, a grid search is implementable. Nevertheless, most contour plots in this study suggests that the asymptotic variance seems to be bowl-shaped in the two planning variables. This implies that a derivative-free optimization algorithm might be able to locate the global optimum. If we allow for more planning variables such as the test duration and the measurement frequency, a well-designed optimization algorithm is more than necessary. In both examples considered in the study, the degradation failure is defined as the event that the degradation exceeds a given threshold. Therefore, the initial degradation affects the failure time. In many applications, the degradation-threshold failure is defined in terms of the degradation increment. For example, the failure of an LED is defined in terms of the percentage of decrease in the lumen maintenance compared with the initial value. In this scenario, the initial degradation level can be regarded as a covariate that partially explains the degradation slope. Strategic allocation for this problem setting is worth investigation.