Remaining useful life prediction based on degradation signals using monotonic B-splines with infinite support
Remaining useful life prediction based on degradation signals using monotonic B-splines with infinite support
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DOI：
10.1080/24725854.2019.1630868
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发表时间：
2019-08
影响因子：
2.6
通讯作者：
Salman Jahani;R. Kontar;Shiyu Zhou;D. Veeramani
中科院分区：
文献类型：
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作者：
Salman Jahani;R. Kontar;Shiyu Zhou;D. Veeramani
摘要退化建模传统上依赖于监测退化信号来对潜在的退化过程进行建模。在该上下文中，故障通常被定义为退化信号达到预先指定的阈值电平的点。许多模型假设退化信号在故障阈值之外完全观察到，而截断退化信号的问题仍然是一个挑战。此外，基于退化过程的物理基础，退化信号应该固有地是单调的。然而，几乎不可避免的是，大多数基于传感器的退化信号都受到噪声的影响，这可能会导致误导预测结果。本文提出了一种在混合效应环境下利用B-样条法对退化信号进行建模和预测的非参数方法。为了处理被截断的历史退化信号的问题，我们的方法是基于用无限支撑函数来扩充B-样条基函数。此外，为了在噪声环境下更准确和更稳健地对退化信号进行建模，推导了确保建模信号单调演化的充要条件。文中还给出了考虑导出单调性约束的混合效应模型随机系数在线更新的适当步骤。通过基于数值研究的分析和使用汽车铅酸蓄电池的真实数据的案例研究，对所提出的框架的性能进行了研究和基准测试。
Abstract Degradation modeling traditionally relies on monitoring degradation signals to model the underlying degradation process. In this context, failure is typically defined as the point where the degradation signal reaches a pre-specified threshold level. Many models assume that degradation signals are completely observed beyond the failure threshold, whereas the issue of truncated degradation signals still remains a challenge. Moreover, based on the physics of a degradation process, the degradation signal should be inherently monotonic. However, it is almost inevitable that most of the sensor-based degradation signals are subject to noise, which can lead to misleading prediction results. In this article, a non-parametric approach to modeling and prognosis of degradation signals using B-splines in a mixed effects setting is proposed. In order to deal with the issue of truncated historical degradation signals, our approach is based on augmenting B-spline basis functions with functions of infinite support. Moreover, to model the degradation signal more accurately and robustly in a noisy setting, necessary and sufficient conditions to ensure monotonic evolution of the modeled signals are derived. Appropriate procedures for online updating of random coefficients of mixed effects model considering derived monotonicity constraints based on degradation data collected from an in-service unit are also presented. The performance of the proposed framework is investigated and benchmarked through analysis based on numerical studies and a case study using real-world data from automotive lead-acid batteries.