Accelerated Degradation Model Research Articles

An improved accelerated degradation model for LED reliability assessment with self-heating impacts

Light-emitting diodes (LEDs) are a solid-state light source being used in numerous applications, including display, communications, medical services, etc. However, the reliability assessment of LED components is still challenging due to the growing up of the LED complexity and/or the miniaturization of assembly technologies. To face this challenge, this paper proposes a novel accelerated degradation testing (ADT) model considering the self-heating impact in the degradation process of a LED component. So, the self-heating impact is first analyzed and modeled. In fact, the junction temperature of a LED component depends not only on the heat generation (e.g., drive current, dispersing heat) but also on the current state (degradation level) of the component. Then, a modified stochastic difference equation is developed for modelling the degradation process by considering the self-heating impact. The LED reliability formulation is finally derived. In addition, an estimation method based on the maximum likelihood is developed to estimate the proposed model's parameters from experimental data. To validate our models, a case study for LED light sources is implemented. The obtained results show that, compared to the TM-21 standard and the conventional ADT methods, our proposed approach achieves better results in the LED reliability assessment.

Nonlinear Doubly Wiener Constant-Stress Accelerated Degradation Model Based on Uncertainties and Acceleration Factor Constant Principle

Although Wiener process models with the consideration of uncertainties, which are nonlinearity, random effects, and measurement errors, have been developed for lifetime prediction in the accelerated degradation test (ADT), they fail to describe the real degradation process because these models assume that the drift parameter correlates with the applied stress, while the diffusion parameter is constant. This paper put forward a nonlinear doubly Wiener constant-stress accelerated degradation model, where both diffusion and drift parameters were compatible with the applied stress according to the acceleration factor constant principle. When degradation data were available, we obtained the unknown parameters by applying a maximum likelihood estimation (MLE) algorithm in the constant-stress ADT (CSADT) model taking uncertainties into account. In addition, the proposed model’s effectiveness was validated through an illustrative example, and an application to the traveling wave tube (TWT) was carried out to demonstrate the superiority of our model in practical applications.

Objective Bayesian analysis of accelerated degradation models based on Wiener process with correlation

In this paper we propose an objective Bayesian method to analyze the accelerated degradation model based on the new Wiener process stated in Ye, Chen, and Shen (Reliab. Eng. Syst. Safe., 2015, 139: 58–67). The Jeffreys prior and reference priors are derived after reparameterization, and the posterior properties under each prior are validated. A Metropolis random walk algorithm is then employed to generate samples from the posterior distributions, and a simulation study is carried out to reveal the frequentist properties of these priors. Finally, we apply the approach to a real data and obtain the mean-time-to-failure (MTTF) under the usage condition.

Optimization Design of Step Stress Accelerated Degradation Test for Motorized Spindle Based on Ds-Optimality

In the accelerated degradation test (ADT) of motorized spindles, it is necessary to apply a variety of stresses to simulate real working conditions. However, the traditional accelerated test scheme optimization method does not consider the weight of various stresses in the test, resulting in the evaluation accuracy of important stress parameters in the model being too low. In order to solve this problem, an optimal design method of the step stress accelerated degradation test (SSADT) scheme for motorized spindles is proposed based on Ds-optimality. Firstly, the fault tree analysis (FTA) method is used to analyze the collected fault data of motorized spindles and screen the main stress. Then, the accelerated degradation model is established by using drift Brownian motion. Based on the Ds-optimality, the optimization variables and constraints in the test are determined, and the optimization model is established with the objective of minimizing the estimated variance of the main stress parameters in the acceleration model; additionally, the optimization steps are given. Finally, an example is given to verify the effectiveness of the method. Sensitivity analysis of the optimization results shows that the method has good robustness.

Research on Reliability Life Evaluation of Torque Motor based on Performance Degradation

The torque motor problem of reliability. The reliability evaluation of torque motor under the condition of accelerated degradation test is studied. Firstly, the failure mechanism of torque motor is studied, the performance degradation parameters are determined, then the theory of accelerated degradation test is described, the type of accelerated degradation model and the conditions of accelerated test are described. The method and procedure of product reliability analysis using accelerated degradation trajectory method under accelerated degradation test are studied. Finally, the reliability of a torque motor is evaluated and the average life value above 0.9 is calculated

A doubly accelerated degradation model based on the inverse Gaussian process and its objective Bayesian analysis

ABSTRACT The accelerated degradation test (ADT) is an effective method for evaluating the lifetime of high-reliability products. In this paper, a doubly accelerated degradation model based on the inverse Gaussian process is proposed to characterize the ADT data, and then an objective Bayesian approach is presented to analyze the model. Some important noninformative priors including the Jeffreys prior and reference priors under different group orderings are derived. The propriety of the posterior distributions under each prior is validated. A simulation study is carried out to show the superiority of objective Bayesian approach compared with the parametric Bootstrap method. Finally, the approach is applied to analyze a carbon film data.

Reliability modeling-based tolerance design and process parameter analysis considering performance degradation

Tolerance design and process parameter analysis have been widely used for reducing the fluctuation of product quality characteristics (QCs). Most existing works focus on finding the best compromise between manufacturing cost and tolerance of QCs. However, the performance degradation of products will lead to QCs deviating from design specifications, which is ignored in current researches. Motivated by the stress relaxation process of helical springs, we propose a more reliable tolerance design and process parameter analysis considering performance degradation. An accelerated degradation model is first constructed to reflect the degradation process of helical springs and the influence of the initial free length. Subsequently, considering the given reliability constraint, a tolerance design method based on degradation performance is developed to guide the quality improvement of helical springs. Furthermore, a new reliability model embodied with the influence of parameter fluctuation is proposed, and an allowable fluctuation range for the deviation and variance of the initial free length is derived, based on which the process parameter can be optimized to improve the manufacturing process. Finally, a real case study of helical springs is conducted to illustrate the implementation and effectiveness of the proposed method.

Bayesian inference for a novel hierarchical accelerated degradation model considering the mechanism variation

For highly reliable products, accelerated thermal degradation tests are efficient to provide feedback on reliability information. In accelerated thermal degradation tests, the degradation data collected at the elevated temperatures are used to extrapolate the performance of products at the normal temperature. An important tool in such extrapolation is the Arrhenius model, in which the activation energy is generally assumed to be constant. However, in some practical accelerated thermal degradation tests of polymeric materials, a variation of the underlying degradation mechanism is induced when the temperature rises to a certain high level, resulting in a change in the activation energy. Motivated by this phenomenon, we propose a two-stage Arrhenius model. The two stages correspond to the lower and higher temperature ranges with different activation energies. Then, this new model is incorporated to the degradation model, yielding a novel hierarchical model for the accelerated thermal degradation test data from polymeric materials involving a mechanism variation. Furthermore, the Bayesian method is adopted for parameter inference, and the lifetime distribution is obtained subsequently. A practical example of polysiloxane rubbers demonstrates the effectiveness of the proposed model.

Multi-objective optimization design of accelerated degradation test based on Wiener process

A multi-objective optimization method for the accelerated degradation test based on Wiener process is proposed in this article in order to solve the problem that a single objective optimization cannot solve the difficulty or even conflicting test configurations caused by different optimization objective functions. An accelerated degradation model is established based on Wiener process, and the unknown parameters are solved by a two-step maximum likelihood estimation method. Considering both the accuracies of life estimation and the model parameter estimation, a multi-objective optimization model is established with the optimization goals of the minimum asymptotic variance of P-quantile of lifetime and the maximum determinant of Fisher information matrix. The Pareto solutions are set by the multi-objective genetic algorithm, and the test configurations for multi-objectives are obtained. Under the step stress accelerated degradation test and the constant stress accelerated degradation test, the effectiveness of the proposed method is verified by an optimization example of LEDs accelerated degradation test.

Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension

Accelerated degradation testing (ADT) has been widely used for the reliability and lifetime evaluations of highly reliable products. Generally speaking, the obtained ADT data are composed of two parts - the deterministic degradation trend and uncertainties. When only limited ADT data can be obtained, there will be a lack of knowledge on recognizing the population and lead to epistemic uncertainties. However, current accelerated degradation models fail to clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions. Motivated by this problem, this paper constructs a new uncertain accelerated degradation model based on uncertainty theory and belief reliability theory, and presents the uncertain statistical method for parameter estimations. An application case is used to illustrate the proposed methodology and conduct discussions for the deterministic degradation trend and uncertainty analyses of the proposed methodology and the analyses of the proposed methodology to the data sizes in time and unit dimensions. Results show that under limited data sizes in time and unit dimensions, the proposed model can clearly distinguish and properly quantify the epistemic uncertainties in time and unit dimensions and is more suitable for the ADT modeling and analysis than the other two widely used accelerated degradation models.

Optimal plan for Wiener constant-stress accelerated degradation model

This paper explores inferential procedures for the Wiener constant-stress accelerated degradation model under degradation mechanism invariance. The exact confidence intervals are obtained for the parameters of the proposed accelerated degradation model. The generalized confidence intervals are also proposed for the reliability function and pth quantile of the lifetime at the normal operating stress level. In addition, the prediction intervals are developed for the degradation characteristic, lifetime and remaining useful life of the product at the normal operating stress level. The performance of the proposed generalized confidence intervals and the prediction intervals is assessed by the Monte Carlo simulation. Furthermore, a new optimum criterion is proposed based on minimizing the mean of the upper prediction limit for the degradation characteristic at the design stress level. The exact optimum plan is also derived for the Wiener accelerated degradation model according to the proposed optimal criterion. The proposed interval procedures and optimum plan are the free of the equal testing interval assumption. Finally, two examples are provided to illustrate the proposed interval procedures and exact optimum plan. Specifically, based on the degradation data of LEDs, some interval estimates of quantities related to reliability indicators are obtained. For the degradation data of carbon-film resistors, the optimal allocation of test units is derived in terms of the proposed optimal criterion.

Modeling of degradation data via wiener stochastic process based on acceleration factor constant principle

This paper proposes a systematic method of modeling accelerated degradation data based on the acceleration factor constant principle. Wiener stochastic process is considered because it is the most extensively used for degradation modeling. For the Wiener stochastic processes with three different time functions, the parameter relationships, which should be satisfied under any two different stress levels, are deduced according to the acceleration factor constant principle. The deduced parameter relationships indicate the stress-related parameters, which are applied to establish accurate accelerated degradation models. In addition, the deduced parameter relationships provide a guidance to test the consistency of the degradation mechanisms under different stress levels. A hypothesis method based on Analysis of Variance is adopted to identify the accelerated stress levels with different degradation mechanism. The degradation data under these stress levels should not be used to assess the product's reliability. The methods of validating accelerated degradation models and reliability assessments are also proposed. The simulation results prove the feasibility and effectiveness of the proposed methods. From the numerical example, it is concluded that the accelerated degradation model established based on the acceleration factor constant principle is more credible and accurate.

Lifetime prediction for carbon fiber composite core wire based on accelerated degradation test

To reduce the huge loss caused by the failure of long-term transmission wire, proper method for the life time prediction is imminent. In this paper, to accomplish long-term service time prediction of carbon fiber composite core (CFCC) wire, an accelerated degradation test is conducted, where 15 samples are allocated to three temperature levels and the degradation data sets under different condition are obtained. Based on the test results, an accelerated degradation model is constructed where Arrhenius function is incorporated to characterize the relationship of degradation rate and temperature. Finally, the median service life and 95% reliable service life of CFCC wire for several conditions is predicted based on the test results and the accelerated degradation model.

Reliability prediction of rotary encoder based on multivariate accelerated degradation modeling

The rotary encoder is an important sensor for measuring rotational motion. A step–stress accelerated degradation test (SSADT) is conducted with respect to accuracy to evaluate the reliability rapidly. The existing errors in output signals are identified as degradation indicator to characterize performance degradation. A methodology for the encoder SSADT modeling is presented. Wiener process theory with unit–to–unit variation is utilized in the model to describe the degradation process of each error. Considering the correlation among the degradation processes, a multivariate degradation modeling method is developed based on the Copula function to estimate the reliability of rotary encoder with multiple degradation indicators. The reliability of the encoder under the use–stress level is derived by combining the margin distributions and Copula function.

Stochastic accelerated degradation model involving multiple accelerating variables by considering measurement error

In accelerated degradation tests, products are usually exposed to several environmental variables or operating conditions. This motivates the need for developing an accelerated degradation model involving multiple accelerating variables. Among the current literature, the conventional accelerated degradation models involving multiple accelerating variables have not considered the measurement error, which inevitably exists in practical degradation datasets. Therefore, a Wiener process-based accelerated degradation model that simultaneously considering multiple accelerating variables and measurement error is proposed. Then approximate closed-form expressions for the failure time distribution (FTD) and its percentiles are derived. The expectation maximization (EM) algorithm is adopted to estimate unknown parameters. Moreover, a multivariate normality testing method is developed to test the fitting goodness of the degradation model. Finally, a comprehensive simulation study and a real application are given to validate the proposed method. The result shows that the proposed model can provide precise estimates even for small sample size of approximately five, and the estimated mean square errors (MSEs) of the mean time to failure (MTTF) and the FTD percentile of the proposed model can be improved by at least 70 % compared with those of the reference methods when the sample size is same.

Ocena niezawodności z wykorzystaniem stochastycznego modelu wzrostu temperatury w metalowych wytłoczkach, uwzględniającego wielorakie zmienne naprężeniowe oraz efekty losowe

Many products wear out over time even before they fail or stop working, therefore, through accelerated degradation tests one is able to make inferences about statistical parameters or the distributions of a product useful life. Since many devices experience different types of variation due to unobservable factors during the manufacturing processes or under certain operating conditions; these situations lead to the need in developing accelerated degradation models with several variables of acceleration and random effects. The proposed model in this paper, is a model based on the gamma process with random effects to have a better analysis of degradation. This model is applied to the analysis of the temperature increase of metal stampings that are affected by multiple explanatory variables. In addition, a statistical inference method based on a Bayesian approach is used to estimate the unknown parameters to then perform a reliability analysis after obtaining the first-passage time distributions.

Enhanced Chemical Stability, Intestinal Absorption, and Intracellular Antioxidant Activity of Cyanidin-3-O-glucoside by Composite Nanogel Encapsulation.

A composite nanogel was developed for cyanidin-3-O-glucoside (C3G) delivery by combining Maillard reaction and heat gelation. The starting materials utilized were ovalbumin, dextran, and pectin. C3G-loaded nanogel was spherical with a diameter of ∼185 nm, which was maintained over a wide range of pH and NaCl concentrations. The composite nanogel enhanced the chemical stability of C3G under accelerated degradation models and a simulated gastrointestinal tract. Clathrin-mediated, caveolae-mediated, and macropinocytosis-related endocytosis contributed to the higher cellular uptake of nano-C3G than that of free-C3G. The apparent permeability coefficients of C3G increased 2.16 times after nanoencapsulation. The transcytosis of the C3G-bearing nanogel occurred primarily through the clathrin-related pathway and macropinocytosis and followed the "common recycling endosomes-endoplasmic reticulum-Golgi complex-basolateral plasma membrane" route. Moreover, nano-C3G was more efficient in restoring the viability of cells and activities of endogenous antioxidant enzymes than free-C3G in oxidative models, which may be attributed to the former's high cellular absorption.

Modeling Accelerated Degradation Data Based on the Uncertain Process

Accelerated degradation testing (ADT) aids the reliability and lifetime evaluations for highly reliable products. In engineering applications, the number of test items is generally small due to finance or testing resource constraints, which leads to the rare knowledge to evaluate reliability and lifetime. Consequently, the epistemic uncertainty is embedded in ADT data and the large-sample based probability theory is no longer appropriate. In this paper, we introduce the uncertainty theory, which is a theory different from the probability theory, to account for such uncertainty due to small samples and build up a framework of ADT modeling. In this framework, an uncertain accelerated degradation model is first proposed based on the arithmetic Liu process. Then, the uncertain statistics for parameter estimations are presented correspondingly, which is completely constructed on objectively observed ADT data. An application case and a simulation case are used to illustrate the proposed methodology. With further comparisons to the Wiener process based accelerated degradation model (WADM) and the Bayesian-WADM, the sensitivities of these models to sample sizes are explored and the results show that the proposed model is superior to the other two probability-based models under the small sample size.

Statistical analysis for the doubly accelerated degradation Wiener model: An objective Bayesian approach

Accelerated degradation test (ADT) provides an effective way to access the reliability of high-reliablity products. In this paper, a doubly accelerated degradation model based on the Wiener process is proposed to characterize the degradation path. An objective Bayesian approach is used to make inferences on the parameters of the model. Important objective priors including the Jeffreys prior and reference priors are derived for the parameters. It is shown that the corresponding posterior distributions are all proper, which implies that these priors enables posterior inferences. A simulation study is adopted to show the effectiveness of the proposed approach. Finally, the model is applied to analyze a real data set.

Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells

Accelerated cycle life testing of lithium-ion batteries is conducted as a means to assess whether a battery will meet its life cycle requirements. This paper presents a study to identify optimal accelerated cycle testing conditions for LiCoO2-graphite cells. A full factorial design of experiment with three stress factors—ambient temperature (10 °C, 25 °C, 45 °C, 60 °C), discharge current rate (C-rate, 0.7C, 1C, 2C), and charge cut-off C-rate (C/5, C/40)—is used to study the effects of these stress factors on battery capacity fade and to obtain the data necessary for decision making. An empirical accelerated degradation model is then developed to capture the characteristics of the two-stage capacity degradation process, along with an accelerated test planning approach.