Accelerated Degradation Testing Data Research Articles

Life Prediction of DC Motor using Time Series Analysis based on Accelerated Degradation Testing

This study presents a method of life prediction for DC motor using time series modeling procedure based on DC motor accelerated degradation testing data. DC motor accelerated degradation data are treated as time series and stochastic process are utilized to describe the degradation process for life prediction. An accelerated degradation test is processed for DC motor until they failed and the accelerated degradation data are collected for life prediction. A comparison between the predicted lifetime and the real lifetime of DC motors is processed and the results show that the life prediction of DC motors using time series analysis is effective.

Step-stress accelerated degradation test modeling and statistical analysis methods

In order to get a rapid assessment on the storage reliability of high-reliable and long-life products within the storage period, accelerated degradation test data with a large amount of reliability information of product is adopted. Conducting a constant-stress accelerated degradation test(CSADT) is generally very costly as it requires a large sample size and long time for test. To overcome this problem, it is necessary to carry out research on modeling and statistical analysis methods of step-stress accelerated degradation test (SSADT). Taking electrical connectors as the object, a research is conducted on statistical model and assessment method for SSADT. On the basis of mixed-effect degradation path model, the statistical model of SSADT for electrical connectors is presented, the maximum likelihood method for SSADT data based on mixed-effect degradation model is proposed. SSADT accelerated by temperature stress is conducted to Y11X-1419 type of electrical connectors, and the storage reliability is assessed with the SSADT data. Compared with the result obtained from accelerated life test, the reliability estimation of 32-year storage period for electrical connectors obtained from SSADT data only have a difference of 0.869%, which validates the accuracy of the degradation model and the feasibility of the test data statistic analysis method put forward.

Electronics Life Prediction based on Multiple Performance Parameters ADT using Multidimensional Time Series Model

This paper proposes a new Accelerated Degradation Testing (ADT) life prediction method utilizing a multidimensional composite time series modeling procedure to take into account the integrated effect of system’s multiple performance parameters along with the random effect of environmental variables for equivalent damage in ADT. In this paper, system performance parameter ADT data are treated as a multidimensional composite time series model to predict system failure time. First, this paper decomposes these multiple performance parameters useful for ADT into three classes as trend, cyclical or random components, and describes them with a combined multi-dependent variable regressive model, hidden periodic model and multivariate auto-regression model. Second, according to standard practice, this paper assumes that the failure of such a system obeys a competing failure rule, that is, for an individual unit there is one primary controlling variable that will indicate failure even though others degrade they do not meet any failure criterion. Failure time at each test-stress level is predicted by using the best linear unbiased prediction of the multidimensional composite time series model. Finally, the reliability at use-stress level is estimated from a failure time distribution evaluation based on the failure time predictions at each test-stress level providing a relationship between failure time and test-stress levels.

A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information

Accelerated degradation testing (ADT) is a common approach in reliability prediction, especially for products with high reliability. However, oftentimes the laboratory condition of ADT is different from the field condition; thus, to predict field failure, one need to calibrate the prediction made by using ADT data. In this paper a Bayesian evaluation method is proposed to integrate the ADT data from laboratory with the failure data from field. Calibration factors are introduced to calibrate the difference between the lab and the field conditions so as to predict a product's actual field reliability more accurately. The information fusion and statistical inference procedure are carried out through a Bayesian approach and Markov chain Monte Carlo methods. The proposed method is demonstrated by two examples and the sensitivity analysis to prior distribution assumption.

Statistical Analysis on Accelerated Degradation Test Data Based on Multiple Performance Parameters

In view of the multiple performance parameters accelerated degradation and need to consider its dependence, we should know how to statistical analysis on the data of testing, establish multiple performance parameters degradation path model and its distribution model and the accelerated model of degradation rate. The multiple performance parameters degradation data’s statistics has two different situations which are independent and dependent. So, A method is proposed which is multiple performance parameters of reliability comprehensive evaluation method based on least squares parameters estimation. This method can achieve rapid and effective assessment of their reliability by using an efficient Monte-Carlo simulation method to get the performance parameters degradation data.

A hierarchical modeling approach to accelerated degradation testing data analysis: A case study

AbstractIn this case study, we investigate the degradation process of light‐emitting diodes (LEDs), which is used as a light source in DNA sequencing machines. Accelerated degradation tests are applied by varying temperature and forward current, and the light outputs are measured by a computerized measuring system. A degradation path model, which connects to the LED function recommended in Mitsuo (1991), is used in describing the degradation process. We consider variations in both measurement errors and degradation paths among individual test units. It is demonstrated that the hierarchical modeling approach is flexible and powerful in modeling a complex degradation process with nonlinear function and random coefficient. After fitting the model by maximum likelihood estimation, the failure time distribution can be obtained by simulation. Copyright © 2010 John Wiley & Sons, Ltd.

Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

Accelerated degradation testing (ADT) expedites product degradation by stressing the product beyond its normal use. To extrapolate the product's reliability at use condition, the ADT requires a known functional link relating the harsh testing environment to the usual use environment. Practitioners are often faced with a great challenge to designate an explicit form of the stress-degradation relationship <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> in accelerated degradation models. In this paper, we propose three methods to make direct inference on the lifetime distribution itself without invoking arbitrary assumptions on the degradation model: delta approximation, multiple imputation of failure-times, and the lifetime distribution-based (LDB) method. The methods are easy to implement without computational difficulty, hence they have potential in a wide range of applications for estimating lifetime distributions from ADT data. We applied the methods to two ADT data sets including a real application of commercial organic light-emitting diodes (OLED). The analysis of the examples and simulation results suggests parametric LDB and multiple imputation method as more potential alternatives to traditional failure-time approaches, especially for the case where there is neither enough physical background, nor historical evidence supporting presumed relationships between stress and the parameters of the degradation model.

Modelling accelerated degradation data using Wiener diffusion with a time scale transformation.

Engineering degradation tests allow industry to assess the potential life span of long-life products that do not fail readily under accelerated conditions in life tests. A general statistical model is presented here for performance degradation of an item of equipment. The degradation process in the model is taken to be a Wiener diffusion process with a time scale transformation. The model incorporates Arrhenius extrapolation for high stress testing. The lifetime of an item is defined as the time until performance deteriorates to a specified failure threshold. The model can be used to predict the lifetime of an item or the extent of degradation of an item at a specified future time. Inference methods for the model parameters, based on accelerated degradation test data, are presented. The model and inference methods are illustrated with a case application involving self-regulating heating cables. The paper also discusses a number of practical issues encountered in applications.