Accelerated Degradation Testing With Long-Term Memory Effects
| Autor: | Wujun Si, Yunfei Shao, Wei Wei |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
Fractional Brownian motion Long-term memory Stochastic process Computer science 0211 other engineering and technologies 02 engineering and technology Asset (computer security) Reliability engineering Benchmark (computing) Electrical and Electronic Engineering Safety Risk Reliability and Quality Reliability (statistics) Statistical hypothesis testing Degradation (telecommunications) |
| Zdroj: | IEEE Transactions on Reliability. 69:1254-1266 |
| ISSN: | 1558-1721 0018-9529 |
| DOI: | 10.1109/tr.2020.2997404 |
| Popis: | The accelerated degradation testing (ADT) has been widely applied as an efficient strategy to obtain the reliability (life) information of the assets in a shorter-than-normal period of time by exposing the assets to higher-than-normal stresses. Recently, with advances in the sensor technology, it has been revealed that the degradation of some assets demonstrates a long-term memory effect, which implies that the future degradation process not only depends on the current degradation state but also strongly correlates with the past degradation history across a long period of time, and the degradation increments are correlated for nonoverlapping time intervals. The existing ADT methods do not consider the long-term memories, which could lead to biased life testing results. In this article, we propose a novel ADT model by integrating the long-term degradation memory effect based on a utilization of the fractional Brownian motion. A maximum likelihood approach is developed to estimate the model parameters. A likelihood-ratio hypothesis test is designed to test the existence of long-term memories. Simulation studies are implemented to illustrate the developed methods. Physical experiments on accelerated testing of a photocatalyst are designed and conducted to demonstrate the proposed model and its advantage over benchmark approaches. The results show that the traditional ADT paradigm, which ignores the long-term memories, significantly underestimates asset lifetime uncertainties. |
| Databáze: | OpenAIRE |
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