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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (7): 227226-227226.doi: 10.7527/S1000-6893.2023.27226

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Measurement uncertainty evaluation method for accelerated degradation testing

Hongxuan GUO1,2, Fuqiang SUN1,2()   

  1. 1.School of Reliability and Systems Engineering,Beihang University,Beijing  100191,China
    2.Science & Technology on Reliability and Environmental Engineering Laboratory,Beijing  100191,China
  • Received:2022-04-01 Revised:2022-05-05 Accepted:2022-05-20 Online:2023-04-15 Published:2022-06-09
  • Contact: Fuqiang SUN E-mail:sunfuqiang@buaa.edu.cn
  • Supported by:
    Stable Supporting Fund of Science &Technology on Reliability and Environmental Engineering Laboratory(WDZC20220101);Fundamental Research Funds for the Central Universities(YWF-22-L-824)

Abstract:

Measurement dispersion, instrument errors and other factors bring about the measurement uncertainty problem in the accelerated degradation test. We propose a propagation and evaluation method for the measurement uncertainty of the accelerated degradation test to improve the accuracy and credibility of the test evaluation results. The sources of measurement uncertainty in accelerated degradation tests are firstly introduced and the propagation effect of measurement uncertainty in the parameter estimation process of the Wiener process accelerated degradation model analyzed. The Guide to the expression of Uncertainty in Measurement (GUM) method and Monte Carlo method (MCM) are then used respectively to evaluate the influence of performance observation data measurement uncertainty on product reliability evaluation results at normal stress levels. Finally, the accelerated degradation test of an actual type polymer sensor is conducted to verify the proposed methods, and comparative analysis of the results verifies the effectiveness of the proposed methods.

Key words: accelerated degradation testing, measurement uncertainty, GUM, Monte Carlo method, polymer sensor

CLC Number: