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

Model Uncertainty in Accelerated Degradation Testing Analysis

“…In this paper, we specifically concentrated on the model uncertainty in accelerated degradation testing analysis with degradation data only. For the situation also with failure time data, readers are referred to [27], [40], and [41].…”

“…The convergence property of the sampling chains will be checked by the Gelman-Rubin index, which is the degree of approximating 1 [47]. Applications can be found in [41] and [48]. When it is converged, a fixed number of samples can be generated from the posterior functions of the parameter vector after a burn-in period (e.g., the first 1000 samples), i.e., θ Mentioned that parameter of the priors in (26) are calibrated using MLE estimates based on actual data, and then updated on the basis of the same data used to formulate the likelihood in (27), which is not fully Bayesian approach.…”

“…For example, Wang et al [40] chose exponential and Weibull lifetime distributions as the reference models for Device-A ALT data; then, a calibration factor is introduced to update the reference models with the field failure data since the lab-test environment and field conditions are different. A similar procedure is used for ADT models in [41].…”

Residual life prediction for highly reliable products with prior accelerated degradation data

“…To resolve the problem, we adopted a Bayesian method to obtain more credible posterior estimates of µ σ , ,rˆˆ by making full use of historical accelerated degradation data. Although the application of Wiener processes in Bayesian inference has been widely studied in literature, most works assume that the random parameters of a Wiener process obey the following conjugate prior [25,34].…”

A reliability analysis method of cloud theory – Monte Carlo based on performance degradation data

“…The metal covering current leakage and hydrophobic on the surface of lightning rod was measured with the increase of time by soaking the lightning rod in salt water at 90°C and performance degradation and time relations as well as the reliability of the over time was obtained in the literature [2]. As accelerated degradation tests carried out for products in the lab fail to achieve the use of real environment, the integrated field-use and laboratory accelerated degradation test information was put forward in the literature [26] for Bayesian method of reliability assessment and markov chain Monte Carlo method. Considering the difficult problems of data extraction by sequential degradation product in complex dynamic failure mode, the implementation of the product, and the reliability and prediction in the long-term level, the reliability of the product degradation and state level predicted by the complex neurons were proposed using feed forward neural network of multilayer in literature [13].…”