Event logs, comprising data on the occurrence of different types of events and associated times, are commonly collected during the operation of modern industrial machines and systems. It is widely believed that the rich information embedded in event logs can be used to predict the occurrence of critical events. In this paper, we propose a recurrent neural network model using time‐to‐event data from event logs not only to predict the time of the occurrence of a target event of interest, but also to interpret, from the trained model, significant events leading to the target event. To improve the performance of our model, sampling techniques and methods dealing with the censored data are utilized. The proposed model is tested on both simulated data and real‐world datasets. Through these comparison studies, we show that the deep learning approach can often achieve better prediction performance than the traditional statistical model, such as, the Cox proportional hazard model. The real‐world case study also shows that the model interpretation algorithm proposed in this work can reveal the underlying physical relationship among events.

中文翻译：

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使用事件日志预测关键事件的深度学习方法

通常在现代工业机器和系统运行期间收集事件日志，其中包括有关不同类型事件的发生和相关时间的数据。普遍认为，事件日志中嵌入的丰富信息可用于预测关键事件的发生。在本文中，我们提出了一种使用事件日志中的事件时间数据的递归神经网络模型，该模型不仅可以预测目标目标事件的发生时间，还可以从经过训练的模型中解释导致重要事件发生的时间。到目标事件。为了提高我们模型的性能，利用了处理审查数据的采样技术和方法。所提出的模型已在模拟数据和真实数据集上进行了测试。通过这些比较研究，我们表明，深度学习方法通​​常比传统的统计模型（例如Cox比例风险模型）能够实现更好的预测性能。实际案例研究还表明，本文提出的模型解释算法可以揭示事件之间潜在的物理关系。