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Improved inverse Gaussian process and bootstrap: Degradation and reliability metrics

机译：改进的高斯逆过程和自举：降级和可靠性指标

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摘要

The inverse Gaussian (IG) process is commonly used in modeling monotonically increasing degradation processes. Traditional degradation modeling considers the process parameters as functions of time and environmental conditions. However, in many practical situations, the degradation increment in the next time interval may depend on degradation state at the current time interval. Therefore, in this paper, we propose an improved inverse Gaussian (IIG) process which considers the dependency between degradation increments and prior degradation states. Reliability metrics of the IIG process are estimated and validated using crack length growth data as well as simulated degradation data. Results show that the proposed model provides more accurate reliability metrics than the IG process model. Bootstrap of degradation increments or detrended degradation increments is introduced as a supplementary method to estimate the remaining life probability interval.
机译：高斯逆（IG）过程通常用于对单调递增的退化过程进行建模。传统的降级建模将过程参数视为时间和环境条件的函数。但是，在许多实际情况下，下一个时间间隔的降级增量可能取决于当前时间间隔的降级状态。因此，在本文中，我们提出了一种改进的逆高斯（IIG）过程，该过程考虑了退化增量与先验退化状态之间的相关性。 IIG工艺的可靠性指标是使用裂纹长度增长数据以及模拟退化数据估算和验证的。结果表明，与IG过程模型相比，该模型提供了更准确的可靠性指标。引入退化增量或去趋势退化增量的自举作为补充方法来估计剩余寿命概率间隔。

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