Journal ArticleDOI
Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model
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TLDR
A lifetime prediction method for high reliability tantalum capacitors was proposed in this paper, based on multiple degradation measures and grey model (GM) for analyzing performance degradation data, a two-parameter model based on GM was developed.Abstract:
A lifetime prediction method for high-reliability tantalum (Ta) capacitors was proposed, based on multiple degradation measures and grey model (GM). For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization (PSO) was used. Then, the new PSO-GM(1, 2, ω) optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145 °C. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.read more
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References
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Using Degradation Measures to Estimate a Time-to-Failure Distribution
C. Joseph Lu,William O. Meeker +1 more
TL;DR: In this article, the authors developed statistical methods for using degradation measures to estimate a time-to-failure distribution for a broad class of degradation models, using a nonlinear mixed-effects model and developing methods based on Monte Carlo simulation to obtain point estimates and confidence intervals for reliability assessment.
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Applications of improved grey prediction model for power demand forecasting
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An improved GA and a novel PSO-GA-based hybrid algorithm
TL;DR: Inspired by the natural features of the variable size of the population, a variable population-size genetic algorithm (VPGA) is presented by introducing the ''dying probability'' for the individuals and the ''war/disease process'' forThe population.
Journal ArticleDOI
Inference from Accelerated Degradation and Failure Data Based on Gaussian Process Models
TL;DR: A general accelerated test model is presented in which failure times and degradation measures can be combined for inference about system lifetime, and some specific models when the drift of the Gaussian process depends on the acceleration variable are discussed in detail.
Journal ArticleDOI
Self-generation RBFNs using evolutional PSO learning
TL;DR: A powerful evolutional particle swarm optimization (PSO) learning algorithm is developed that self-generates radial basis function neural networks (RBFNs) to deal with three nonlinear problems.