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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| Vydané v: | Reliability engineering & system safety Ročník 189; číslo September; s. 261 - 270 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.09.2019
Elsevier BV Elsevier |
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | •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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| AbstractList | •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. 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. |
| Author | Berenguer, Christophe Hao, Songhua Yang, Jun |
| Author_xml | – sequence: 1 givenname: Songhua surname: Hao fullname: Hao, Songhua organization: School of Reliability and Systems Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Jun surname: Yang fullname: Yang, Jun email: tomyj2001@buaa.edu.cn organization: School of Reliability and Systems Engineering, Beihang University, Beijing, China – sequence: 3 givenname: Christophe surname: Berenguer fullname: Berenguer, Christophe organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, Grenoble, France |
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| Keywords | Measurement errors The MLE method Extended inverse Gaussian process model Skew-normal random effects Extended MC integration algorithm the MLE method |
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| Snippet | •Extend traditional IG process by incorporating skew-normal random effects.•Derive analytically the lifetime distribution for the proposed EIG process... As an important degradation model for monotonic degradation processes, the inverse Gaussian (IG) process model has attracted a lot of attention. To... |
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| SubjectTerms | 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 |
| Title | Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors |
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