Degradation data analysis based on gamma process with random effects

Wang, Xiaofei; Wang, Bingxing; Hong, Yili; Pei-hua, Jiang

doi:10.1016/j.ejor.2020.11.036

Cited by 39 publications

(18 citation statements)

References 47 publications

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“…Another category of degradation models is stochastic process models. When there are random effects in the stochastic process model (e.g., Wang et al 9 ), Bayesian method can be convenient for handling random effects. In recurrent events, data with window observations (e.g., Hong et al 10 and Min et al 11 ) can also occur and the observation windows can sometime cause problems in estimation.…”

Section: Discussionmentioning

confidence: 99%

Comments on “specifying prior distributions in reliability applications” by Tian, Lewis‐Beck, Niemi, and Meeker

Min

Lin

Hong

2023

Appl Stoch Models Bus & Ind

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Section: Discussionmentioning

confidence: 99%

Comments on “specifying prior distributions in reliability applications” by Tian, Lewis‐Beck, Niemi, and Meeker

Min

Lin

Hong

2023

Appl Stoch Models Bus & Ind

Self Cite

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“…The Gamma and Weibull degradation models are widely used in the literature. 13,14 In the following examples, we consider two typical implied lifetime distributions from model (2).…”

Section: Developments With Additive and Multiplicative Degradation Pathsmentioning

confidence: 99%

Reliability aspects in a dynamic time-to-failure degradation-based model

Kayid

Alshagrawi

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Although the ordinary time-to-failure degradation-based model has been extensively used in practice, it also has its limitations. In this paper, we consider a time-to-failure degradation-based model recently proposed by Albabtain et al., where a limiting conditional survival probability entertains further stochastic relationships between the failure time and the degree of degradation. In the particular case where the limited survival probability is available for the proportional failure rate model, the model is developed using two well-known degradation paths, namely the additive degradation path and the multiplicative degradation path, each of which has a component of random variation. Preservation of various stochastic orders and aging properties of the random variation component in the model in the described setting is developed. To illustrate the model in the modified design, some examples of interest in reliability are presented.

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“…The most widely used stochastic process models include Wiener, 5 Gamma, 6 inverse Gaussian (IG) process, 7 and cumulative impact damage model. 8 Duan et al 9 established an accelerated characteristic degradation model for avionics connectors using a Wiener process with stochastic effects, estimated the model parameters based on the EM algorithm, and calculated the degradation amount and performance reliability.…”

Section: Introductionmentioning

confidence: 99%

Performance reliability degradation analysis and lifetime prediction based on generalized normal grey cloud Bayesian Wiener process

Niu,

Fang

2024

Quality & Reliability Eng

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This study addresses the problem of performance reliability degradation and lifetime prediction in complex systems with high reliability but insufficient information. It comprehensively considers the effects of various uncertainties, such as measurement, cognitive, and stochastic uncertainties, and proposes a generalized normal grey cloud Bayesian Wiener process (GNGCBWP) stochastic degradation model. First, based on the historical degradation information of the system, the interval grey number sequence of the degradation increment is constructed, and the performance degradation path of the system is analyzed to determine the degradation model type. Then, combining the normal grey cloud and Bayesian estimation theory, the relevant definitions and theorems of generalized normal grey cloud Bayesian estimation (GNGCBE) are proposed. Finally, the parameter estimation results obtained using the GNGCBE method are substituted into the grey Wiener process degradation model to dynamically analyze performance reliability degradation and predict the remaining useful life (RUL) in real‐time. And the validity and scientificity of the proposed model is verified through a case study of laser equipment. Moreover, the comparison results of the models indicate that the proposed model has advantages in dynamic analysis of performance reliability degradation and real‐time RUL prediction for highly reliable complex systems with limited or poor degradation information. It can significantly reduce the impact of multiple uncertainties on the analysis results and improve the accuracy and reliability of the final prediction results.

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Degradation data analysis based on gamma process with random effects

Cited by 39 publications

References 47 publications

Comments on “specifying prior distributions in reliability applications” by Tian, Lewis‐Beck, Niemi, and Meeker

Comments on “specifying prior distributions in reliability applications” by Tian, Lewis‐Beck, Niemi, and Meeker

Reliability aspects in a dynamic time-to-failure degradation-based model

Performance reliability degradation analysis and lifetime prediction based on generalized normal grey cloud Bayesian Wiener process

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