Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process
type: Item
instance of: scholarly article
title: Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process
author: Di Liu
author: Shaoping Wang
author: Chao Zhang
published in: Applied Mathematics and Computation
publication date: 2021-12-13T00:00:00Z
Mathematics Subject Classification ID: 62Nxx
Mathematics Subject Classification ID: 62Mxx
Mathematics Subject Classification ID: 60Jxx
zbMATH DE Number: 7442841
zbMATH Keywords: accelerated life testing
zbMATH Keywords: accelerated degradation testing
zbMATH Keywords: artificial neural network
zbMATH Keywords: Wiener process
zbMATH Keywords: Bayesian inference
MaRDI profile type: Publication
full work available at URL: j.amc.2021.126757
OpenAlex ID: W3213970741
cites work: Sequential procedure for software reliability estimation
cites work: An evidence theory based model fusion method for degradation modeling and statistical analysis
cites work: Wiener processes with random effects for degradation data
cites work: A reliability estimation approach via Wiener degradation model with measurement errors
cites work: Optimal design of accelerated degradation tests based on Wiener process models
cites work: Accelerated degradation models for failure based on geometric Brownian motion and gamma processes
DOI: 10.1016/J.AMC.2021.126757
description: scientific article; zbMATH DE number 7442841