Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study

The performance of units in the same batch can exhibit considerable heterogeneity due to the variation in the raw materials and fluctuation in the manufacturing process. For products suffering performance degradation in their use, such heterogeneity often results in an increase in the dispersion of...

Full description

Saved in:
Bibliographic Details
Published in:Reliability engineering & system safety Vol. 216; p. 107897
Main Authors: Wang, Zhijie, Zhai, Qingqing, Chen, Piao
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.12.2021
Elsevier BV
Subjects:
Algorithms
Comparative studies
EM algorithm
Gallium arsenide lasers
Generalized inverse
Generalized inverse Gaussian distribution
Heterogeneity
Heterogeneous degradation
Immunoglobulins
Inverse Gaussian probability distribution
Lasers
Manufacturing industry
Modelling
Parameter estimation
Performance degradation
Probability distribution functions
Raw materials
Reliability engineering
Wiener process model
Heterogeneous degradation
Wiener process model
Generalized inverse Gaussian distribution
EM algorithm
ISSN:0951-8320, 1879-0836
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The performance of units in the same batch can exhibit considerable heterogeneity due to the variation in the raw materials and fluctuation in the manufacturing process. For products suffering performance degradation in their use, such heterogeneity often results in an increase in the dispersion of the degradation paths of units in a population. The degradation rate of products can be unit-specific and often treated as random effects. This paper develops a novel random-effects Wiener process model to account for the unit-to-unit heterogeneity in the degradation, where the generalized inverse Gaussian (GIG) distribution is used to model the unit-specific degradation rate. The GIG distribution is a very general distribution with broad applications, which includes the inverse Gaussian (IG) distribution and the Gamma distribution as special cases. We investigate the model properties and develop an expectation maximization (EM) algorithm for parameter estimation. By comparing the proposed model with existing models on two real degradation datasets of the infrared LEDs and the GaAs lasers, we show that the proposed model is quite effective for degradation modeling with heterogeneous rates.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.107897