A Lifetime Prediction Method for LEDs Considering Real Mission Profiles

Reliability has been a dominant factor in the performance of power converters. In this study, an enhanced k-out-ofn:G model is presented to predict and analyse the reliability of typical asymmetric half-bridge power converter in switched reluctance motor drive. First, the reliability estimation procedure is introduced including electrothermal model, failure rate model, and reliability model, during which the electrothermal model is critical to accelerate the process of reliability prediction. Next, the enhanced k-out-of-n:G model can be built, and corresponding solving process is derived. Meanwhile, the reliability comparison is quantitatively illustrated between conventional k-out-of-n:G models, generally accepted Markov model, and enhanced k-outof-n:G model, indicating the result with the proposed model is consistent with Markov model. Moreover, the modelling and solving processes are simpler. Then, the reliability is optimised in respect to turn-on angle, turn-off angle, and fault-tolerance strategies with the enhanced k-out-of-n:G model, providing a guideline to select the most effective control parameters and strategies for reliability improvement. Finally, the fault experiments and thermal stress measurement are carried out to indirectly validate the reliability analysis.

show abstract

“…The B X lifetime is the time when the fault probability of power converter is X% [23], which is presented in Fig. 1b.…”

Section: Evaluation Procedures Of Converter-level Reliabilitymentioning

confidence: 99%

Converter‐level reliability prediction and analysis in switched reluctance motor drive

Chen

Dong

2020

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…Pan and Wang et al introduced a calibration factor to the lifetime predictive model to denote the environmental influence. Qu et al proposed a mission profile‐based lifetime predictive method to estimate the lifetime and reliability performance of light‐emitting diodes in field operations. Thomas adjusted the lifetime prediction model of lithium‐ion (Li‐ion) cell in order to make it more realistic for the dynamic use condition.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning for predicting degradation under time‐varying environment

Wang

Lu²,

Wang

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

Product lifetime prediction is challenging when the product is subject to a time-varying operational environment. Most of the existing studies use some functions to explicitly specify the relationship between degradation parameters and environmental conditions so as to reveal how the degradation process evolves over time. However, in many applications, the assumptions needed for establishing these functions cannot be validated in engineering practice or they cannot accurately model the entire underlying degradation mechanism. In contrast to previous work, the focus of our study is placed on product degradation prognosis by implementing an ensemble learning method. This method combines the stochastic process modeling approach and the machine learning approach, taking advantage of these approaches to gain a more accurate and stable degradation prediction. The proposed method is demonstrated by some simulation examples and by a case study of lithium-ion battery accelerated degradation test. Both the simulation study and the real case verify the superiority of the proposed method. The case study indicates that the ensemble learning method can further help to effectively manage the energy storage and energy distribution of battery packs. K E Y W O R D Sdegradation process, ensemble model, Li-ion battery, machine learning, stochastic process

show abstract

“…Light-emitting diodes (LEDs) have a high energy efficiency, a long lifetime and environmentally friendly properties compared to other lighting technologies [1,2]. LEDs are extensively used these days in various applications, exemplified in personal cell phones to large commercial advertising display boards.…”

Section: Introductionmentioning

confidence: 99%

92.5% Average Power Efficiency Fully Integrated Floating Buck Quasi-Resonant LED Drivers Using GaN FETs

et al. 2020

View full text Add to dashboard Cite

LEDs are highly energy efficient and have substantially longer lifetimes compared to other existing lighting technologies. In order to facilitate the new generation of LED devices, approaches to improve power efficiency with increased integration level for lighting device should be analysed. This paper proposes a fully on-chip integrated LED driver design implemented using heterogeneous integration of gallium nitride (GaN) devices atop BCD circuits. The performance of the proposed design is then compared with the conventional fully on-board integration of power devices with the LED driver integrated circuit (IC). The experimental results confirm that the fully on-chip integrated LED driver achieves a consistently higher power efficiency value compared with the fully on-board design within the input voltage range of 4.5–5.5 V. The maximal percentage improvement in the efficiency of the on-chip solution compared with the on-board solution is 18%.

show abstract

A Lifetime Prediction Method for LEDs Considering Real Mission Profiles

Cited by 31 publications

References 14 publications

Converter‐level reliability prediction and analysis in switched reluctance motor drive

Converter‐level reliability prediction and analysis in switched reluctance motor drive

Ensemble learning for predicting degradation under time‐varying environment

92.5% Average Power Efficiency Fully Integrated Floating Buck Quasi-Resonant LED Drivers Using GaN FETs

Contact Info

Product

Resources

About