Literature DB >> 9384624

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

G A Whitmore1, F Schenkelberg.   

Abstract

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.

Mesh:

Year:  1997        PMID: 9384624     DOI: 10.1023/a:1009664101413

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  1 in total

1.  Estimating degradation by a Wiener diffusion process subject to measurement error.

Authors:  G A Whitmore
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

  1 in total
  9 in total

1.  Estimation in degradation models with explanatory variables.

Authors:  V Bagdonavicius; M S Nikulin
Journal:  Lifetime Data Anal       Date:  2001-03       Impact factor: 1.588

2.  Inference from accelerated degradation and failure data based on Gaussian process models.

Authors:  W J Padgett; Meredith A Tomlinson
Journal:  Lifetime Data Anal       Date:  2004-06       Impact factor: 1.588

3.  Covariates and random effects in a gamma process model with application to degradation and failure.

Authors:  Jerry Lawless; Martin Crowder
Journal:  Lifetime Data Anal       Date:  2004-09       Impact factor: 1.588

4.  Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Authors:  Chanseok Park; W J Padgett
Journal:  Lifetime Data Anal       Date:  2005-12       Impact factor: 1.588

5.  Failure inference from a marker process based on a bivariate Wiener model.

Authors:  G A Whitmore; M J Crowder; J F Lawless
Journal:  Lifetime Data Anal       Date:  1998       Impact factor: 1.588

6.  Parameter inference from hitting times for perturbed Brownian motion.

Authors:  Massimiliano Tamborrino; Susanne Ditlevsen; Peter Lansky
Journal:  Lifetime Data Anal       Date:  2014-09-04       Impact factor: 1.588

7.  Modeling of BN Lifetime Prediction of a System Based on Integrated Multi-Level Information.

Authors:  Jingbin Wang; Xiaohong Wang; Lizhi Wang
Journal:  Sensors (Basel)       Date:  2017-09-15       Impact factor: 3.576

8.  A General Accelerated Degradation Model Based on the Wiener Process.

Authors:  Le Liu; Xiaoyang Li; Fuqiang Sun; Ning Wang
Journal:  Materials (Basel)       Date:  2016-12-06       Impact factor: 3.623

9.  Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion.

Authors:  Fuqiang Sun; Le Liu; Xiaoyang Li; Haitao Liao
Journal:  Sensors (Basel)       Date:  2016-08-06       Impact factor: 3.576

  9 in total

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