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Weighted-feature and cost-sensitive regression model for component continuous degradation assessment

机译：用于组件连续退化评估的加权特征和成本敏感回归模型

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摘要

Conventional data-driven models for component degradation assessment try to minimize the average estimation accuracy on the entire available dataset. However, an imbalance may exist among different degradation states, because of the specific data size and/or the interest of the practitioners on the different degradation states. Specifically, reliable equipment may experience long periods in low-level degradation states and small times in high-level ones. Then, the conventional trained models may result in overfitting the low-level degradation states, as their data sizes overwhelm the high-level degradation states. In practice, it is usually more interesting to have accurate results on the high-level degradation states, as they are closer to the equipment failure. Thus, during the training of a data-driven model, larger error costs should be assigned to data points with high-level degradation states when the training objective minimizes the total costs on the training dataset. In this paper, an efficient method: is proposed for calculating the costs for continuous degradation data. Considering the different influence of the features on the output, a weighted-feature strategy is integrated for the development of the data-driven model. Real data of leakage of a reactor coolant pump is used to illustrate the application and effectiveness of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.
机译：用于组件降级评估的常规数据驱动模型试图将整个可用数据集的平均估计精度降至最低。然而，由于特定的数据大小和/或从业者对不同的退化状态的兴趣，在不同的退化状态之间可能存在不平衡。具体而言，可靠的设备在低级别的降级状态下可能会经历很长时间，而在高级别的状态下可能会经历少量时间。然后，传统的训练模型可能会导致过度拟合低级退化状态，因为它们的数据大小使高级退化状态不堪重负。在实践中，通常更有趣的是对高水平的退化状态获得准确的结果，因为它们更接近设备故障。因此，在训练数据驱动模型的过程中，当训练目标使训练数据集的总成本最小时，应将较大的错误成本分配给具有高级别退化状态的数据点。本文提出了一种有效的方法：提出了用于计算连续退化数据成本的方法。考虑到特征对输出的不同影响，集成了加权特征策略以开发数据驱动模型。反应堆冷却剂泵泄漏的真实数据用于说明所提出方法的应用和有效性。 （C）2017 Elsevier Ltd.保留所有权利。

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