Degradation Modeling Using Stochastic Processes With Random Initial Degradation

In degradation tests, it is common to see that the initial degradation levels of test units are heterogeneous. Moreover, the degradation rate of a path may also be correlated with the initial value of the degradation measure. Motivated by this observation, in this paper, we introduce a time shift to...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on reliability Jg. 68; H. 4; S. 1320 - 1329
Hauptverfasser: Shen, Lijuan, Wang, Yudong, Zhai, Qingqing, Tang, Yincai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Analytical models
Computer simulation
Correlation
Data models
Degradation
Error analysis
Estimation
Expectation–maximization (EM) algorithm
Gallium arsenide lasers
Gaussian distribution
Gaussian process
maximum likelihood estimation
Modelling
random initial degradation
random-effects model
Stochastic models
stochastic process models
Stochastic processes
ISSN:0018-9529, 1558-1721
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In degradation tests, it is common to see that the initial degradation levels of test units are heterogeneous. Moreover, the degradation rate of a path may also be correlated with the initial value of the degradation measure. Motivated by this observation, in this paper, we introduce a time shift to the traditional stochastic process models, which presumes that the product has experienced some degradation at the beginning of the test. Such a modeling technique can also capture the correlation between the initial degradation and the degradation rate, when the degradation rate of each path does vary from unit to unit. We apply this technique to the three popular stochastic process models, i.e., the Wiener process, the gamma process, and the inverse Gaussian process, and develop the corresponding parameter inference procedures. Monte Carlo simulations are implemented to validate the proposed models and the estimation procedures. Applications to the degradation analysis of block error rates data and GaAs laser data reveal good performance of the proposed models.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2018.2885133