Sequential Bayesian Planning for Accelerated Degradation Tests Considering Sensor Degradation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Original languageEnglish
Pages (from-to)964-974
Number of pages11
Journal / PublicationIEEE Transactions on Reliability
Issue number3
Online published6 Dec 2022
Publication statusPublished - 2023


Most classical accelerated degradation test (ADT) planning models implicitly overlook the errors when measuring the degradation levels of the test units. However, the sensor measurement errors are inevitable and the magnitude of the errors may have a trend to increase over time due to sensor degradation. As a consequence improperly overlooking the sensor degradation in ADT planning could result in a test plan with unsatisfactory performance. This article addresses this issue by proposing a sequential ADT planning model that factors in sensor degradation. The system degradation level is periodically measured, based on which we dynamically adjust the stress level during ADT. We adopt a Bayesian framework that periodically updates the posterior distribution of model parameters considering the sensor degradation. An approximate Bayesian computation algorithm is developed to circumvent the difficulty of directly evaluating the complicated likelihood function in our problem. Numerical studies on a gas turbine reveal that our sequential model outperforms several traditional ADT designs that overlook the sensor degradation.

Research Area(s)

  • Accelerated degradation test, approximate bayesian computation, optimal design, sensor degradation, sequential bayesian planning, MODEL SELECTION, RELIABILITY, DESIGN, DISTRIBUTIONS, SYSTEMS, ALLOCATION, SIGNALS