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.

中文翻译：

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具有长期记忆效应的加速退化测试

加速退化测试 (ADT) 已被广泛用作一种有效的策略，通过将资产暴露在高于正常的压力下，在比正常时间更短的时间内获取资产的可靠性（寿命）信息。最近，随着传感器技术的进步，一些资产的退化表现出长期记忆效应，这意味着未来的退化过程不仅取决于当前的退化状态，而且与过去的退化密切相关。很长一段时间的历史，并且退化增量与非重叠时间间隔相关。现有的ADT方法没有考虑长期记忆，这可能导致寿命测试结果有偏差。在本文中，我们通过基于分数布朗运动的利用整合长期退化记忆效应，提出了一种新的 ADT 模型。开发了一种最大似然方法来估计模型参数。似然比假设检验旨在检验长期记忆的存在。实施模拟研究以说明开发的方法。设计并进行了光催化剂加速测试的物理实验，以证明所提出的模型及其相对于基准方法的优势。结果表明，传统的 ADT 范式忽略了长期记忆，显着低估了资产寿命的不确定性。似然比假设检验旨在检验长期记忆的存在。实施模拟研究以说明开发的方法。设计并进行了光催化剂加速测试的物理实验，以证明所提出的模型及其相对于基准方法的优势。结果表明，传统的 ADT 范式忽略了长期记忆，显着低估了资产寿命的不确定性。似然比假设检验旨在检验长期记忆的存在。实施模拟研究以说明开发的方法。设计并进行了光催化剂加速测试的物理实验，以证明所提出的模型及其相对于基准方法的优势。结果表明，传统的 ADT 范式忽略了长期记忆，显着低估了资产寿命的不确定性。