Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors

•Extend traditional IG process by incorporating skew-normal random effects.•Derive analytically the lifetime distribution for the proposed EIG process model.•Develop MLEs of the model parameters for perfect and perturbed measurements.•Show the advantages of the proposed model through simulation and...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 189; no. September; pp. 261 - 270
Main Authors: Hao, Songhua, Yang, Jun, Berenguer, Christophe
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.09.2019
Elsevier BV
Elsevier
Subjects:
Algorithms
Applications
Automatic
Computer simulation
Crack propagation
Degradation
Economic models
Engineering Sciences
Extended inverse Gaussian process model
Extended MC integration algorithm
Fatigue failure
Fracture mechanics
Gallium arsenide lasers
Gaussian process
Immunoglobulins
Measurement errors
Parameter estimation
Reliability engineering
Service life assessment
Skew-normal random effects
Statistics
The MLE method
Measurement errors
The MLE method
Extended inverse Gaussian process model
Skew-normal random effects
Extended MC integration algorithm
the MLE method
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•Extend traditional IG process by incorporating skew-normal random effects.•Derive analytically the lifetime distribution for the proposed EIG process model.•Develop MLEs of the model parameters for perfect and perturbed measurements.•Show the advantages of the proposed model through simulation and case study. As an important degradation model for monotonic degradation processes, the inverse Gaussian (IG) process model has attracted a lot of attention. To characterize random effects among test samples, the traditional IG process model usually assumes a normal distributed degradation rate. However, the degradation rates in some applications may manifest some asymmetric and non-normal behaviors, such as the GaAs laser degradation data. Therefore, we propose an extended inverse Gaussian (EIG) process model by incorporating skew-normal random effects, and derive its analytical lifetime distribution. Furthermore, considering that available studies about IG process models are limited on the aspect of measurement errors, parameter estimation methods for the proposed degradation model are developed for two scenarios, i.e., the maximum likelihood estimations (MLEs) for perfect measurements, and an extended Monte Carlo (MC) integration algorithm for the MLEs for perturbed measurements. Then a simulation study is adopted to show the effectiveness of the proposed MLEs, and two illustrative examples of GaAs laser degradation and fatigue crack growth are provided to illustrate the advantages of the proposed EIG process model, i.e., the improvement in degradation data fitting performance and lifetime evaluation accuracy by incorporating skew-normal random effects and measurement errors.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2019.04.031