有限可用数据条件下为预测生成实值故障数据

G. Ranasinghe, A. Parlikad
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引用次数: 9

摘要

数据驱动的预测解决方案在故障数据可用性有限的情况下表现不佳,因为故障数据样本的数量不足以有效地训练预测模型。为了解决这个问题,我们提出了一种新的方法来生成实值故障数据,该方法允许增强训练数据集,从而增加故障数据样本的数量。与现有的重复或随机生成数据的数据生成技术相比,所提出的方法能够生成新的和真实的故障数据样本。为此,我们利用了条件生成对抗网络和与故障模式相关的辅助信息。在一个涉及重型卡车空气吹扫阀故障预测的实际案例研究中,对所提出的方法进行了评估。利用梯度增强机和随机森林分类器建立了两种预测模型。结果表明,当这些模型在增强训练数据集上进行训练时,它们的表现大大优于文献中先前为案例研究提出的最佳预测解决方案。更具体地说,故障和误报造成的成本降低了44%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability
Data-driven prognostics solutions underperform under the conditions of limited failure data availability since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating real-valued failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. To this end, we utilised the conditional generative adversarial network and auxiliary information pertaining to the failure modes. The proposed methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy trucks. Two prognostics models are developed using gradient boosting machine and random forest classifiers. It is shown that when these models are trained on the augmented training dataset, they outperform the best prognostics solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%.
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