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An enhanced copula-based method for data-driven prognostics considering insufficient training units

机译：考虑训练单元不足的基于关联词的改进方法，用于数据驱动的预测

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摘要

Data-driven based prognostics typically requires sufficient run-to-failure training units in order to learn degradation characteristics of engineering components or products without the need of understanding the physics based degradation mechanisms. With insufficient training units, however, the model form learned from the training units may be inaccurate, which could result in large remaining useful life (RUL) prediction errors for the actual test units. A recently proposed copula-based sampling method does not assume any degradation model form, but builds a set of statistical correlation models for the RUL prediction, which shows high RUL prediction accuracy with sufficient training units. This paper proposes an enhanced copula-based method to address the instability issue of the method when dealing with insufficient training units. In particular, the sampling part for the RUL prediction in the original time domain is replaced by an analytical formulation in the standard uniform space with multiple copulas so that the stability issue can be addressed. Furthermore, a simplified version of the RUL prediction is proposed with extremely high efficiency. Effectiveness of the enhanced method is demonstrated by two case studies: one for resistance degradation of lead-acid batteries and another for capacity degradation of lithium-ion batteries.
机译：基于数据驱动的预测通常需要足够的从运行到失败的培训单元，以便了解工程组件或产品的退化特性，而无需了解基于物理学的退化机理。但是，如果训练单元不足，则从训练单元学习的模型形式可能会不准确，这可能导致实际测试单元的剩余剩余使用寿命（RUL）预测误差很大。最近提出的基于copula的采样方法不采用任何降级模型形式，而是建立了一组用于RUL预测的统计相关模型，该模型显示了足够的训练单元，具有很高的RUL预测精度。本文提出了一种改进的基于copula的方法来解决该方法在处理训练单元不足时的不稳定性问题。特别是，在原始时域中用于RUL预测的采样部分被具有多个copula的标准均匀空间中的分析公式所替代，因此可以解决稳定性问题。此外，以极高的效率提出了RUL预测的简化版本。通过两个案例研究证明了改进方法的有效性：一个案例用于铅酸电池的电阻退化，另一个案例用于锂离子电池的容量退化。

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