Model Uncertainty in Accelerated Degradation Testing Analysis

Liu, Le; Li, Xiaoyang; Zio, Enrico; Kang, Rui; Jiang, Tongmin

doi:10.1109/tr.2017.2696341

Cited by 47 publications

(11 citation statements)

References 50 publications

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“…. In general, the major constraint for ADT is the experiment budget [28]. Thus, the total test sample number N and total test measurement times M are restricted by each other for the reason that the sample cost and equipment operation cost are the major consumption of budget.…”

Section: ) Plan Space and Optimal Adt Planmentioning

confidence: 99%

Modified Bayesian D-Optimality for Accelerated Degradation Test Design With Model Uncertainty

et al. 2019

IEEE Access

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Accelerated degradation test (ADT) has become the main method to assess system reliability. In ADT, Bayesian D-optimality criterion is an effective objective function to deal with the model parameter uncertainty in designing an ADT plan. Current optimal ADT designs based on Bayesian D-optimality usually assume a single degradation model. However, this assumption sometimes may not be robust enough because the degradation trajectories of the target systems are usually unknown. Thus, besides the model parameter uncertainty, the degradation model uncertainty is also a question worth considering during designing an optimal ADT plan. Integrating the advantage of Bayesian model averaging to Bayesian D-optimality, this paper proposes a modified Bayesian D-optimality to handle the model parameter uncertainty and the model uncertainty simultaneously. Simulations are provided to analyze the robustness of the proposed objective function. The results show that the modified Bayesian D-optimality could effectively deal with the uncertainties of the degradation model and model parameter.INDEX TERMS Bayesian D-optimality criterion, Bayesian model averaging, model uncertainty, optimal ADT design.

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Section: ) Plan Space and Optimal Adt Planmentioning

confidence: 99%

Modified Bayesian D-Optimality for Accelerated Degradation Test Design With Model Uncertainty

et al. 2019

IEEE Access

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“…The long-requested lifetime of products sometimes results in long, time-consuming and expensive verification methods (which are disadvantageous for both the supplier and customer). Shorter but sophisticated indirect verification methods (e.g., accelerated tests) are sometimes hardly acceptable to customer due to their complexity and inherent degree of uncertainty [ 11 ]. Furthermore, part of the challenge is that existing tests and test equipment may unconsciously be biased towards their use in the verification process.…”

Section: Introductionmentioning

confidence: 99%

Test Plan for the Verification of the Robustness of Sensors and Automotive Electronic Products Using Scenario-Based Noise Deployment (SND)

Heinold¹,

Bárkányi

Abonyi

2021

Sensors

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The targeted shortening of sensor development requires short and convincing verification tests. The goal of the development of novel verification methods is to avoid or reduce an excessive amount of testing and identify tests that guarantee that the assumed failure will not happen in practice. In this paper, a method is presented that results in the test loads of such a verification. The method starts with the identification of the requirements for the product related to robustness using the precise descriptions of those use case scenarios in which the product is assumed to be working. Based on the logic of the Quality Function Deployment (QFD) method, a step-by-step procedure has been developed to translate the robustness requirements through the change in design parameters, their causing phenomena, the physical quantities as causes of these phenomena, until the test loads of the verification. The developed method is applied to the test plan of an automotive sensor. The method is general and can be used for any parts of a vehicle, including mechanical, electrical and mechatronical ones, such as sensors and actuators. Nonetheless, the method is applicable in a much broader application area, even outside of the automotive industry.

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“…30,31 In order to access the lifetime evaluation with multi-distribution fusion, the BMA method presents the information by random variables and fuses the predicted results based on the candidate distribution possibilities. [32][33][34][35] Liu et al 36 have used BMA method to combine the candidate models, focusing on degradation process. The ability of the proposed method on the s-credibility degradation data analysis is focused.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Liu

Wang

Zhang

et al. 2019

Advances in Mechanical Engineering

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Rubber sealing ring is one of the most widely used seals. It is always stored for a period of time before put into use, especially in aeronautic and aerospace applications. It is necessary to evaluate the storage lifetime of rubber sealing rings. However, due to the long storage lifetime of rubber sealing rings, two issues need to be handled, including model uncertainty and lack of storage lifetime data. A Bayesian model averaging based storage lifetime assessment method for rubber sealing rings is proposed in this article. The Gamma distribution model and Weibull distribution model are selected as the candidate models and combined based on Bayesian model averaging method. The Bayesian model averaging method is applied to handle the model uncertainty. Considering the lack of storage lifetime data, the degradation data are utilized to give the priors of model probability and distribution parameters based on the similarity principle. The results indicate that the proposed method has smaller minus log-likelihood value and is better than the other discussed method, considering both goodness of fit and complexity.

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Model Uncertainty in Accelerated Degradation Testing Analysis

Cited by 47 publications

References 50 publications

Modified Bayesian D-Optimality for Accelerated Degradation Test Design With Model Uncertainty

Modified Bayesian D-Optimality for Accelerated Degradation Test Design With Model Uncertainty

Test Plan for the Verification of the Robustness of Sensors and Automotive Electronic Products Using Scenario-Based Noise Deployment (SND)

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

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