Accurate estimation of the remaining useful life (RUL) and health state for rollers is of great significance to hot rolling production. It can provide decision support for roller management so as to improve the productivity of the hot rolling process. In addition, the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance. Therefore, a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper. Firstly, a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator (HI) is developed, where the HI is able to indicate the health state of the roller. Following that, a state-space model is constructed to describe the HI, and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold. Finally, application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site, and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.

中文翻译：

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基于深度循环神经网络的热轧带钢轧辊剩余使用寿命预测

准确估算轧辊剩余使用寿命（RUL）和健康状态对热轧生产具有重要意义。它可以为轧辊管理提供决策支持，从而提高热轧过程的生产率。此外，滚子的 RUL 预测有助于从当前的定期维护策略过渡到基于条件的维护。因此，本文提出了一种可以从批量数据中提取粗粒度和细粒度特征来预测滚子RUL的新方法。首先，开发了一种新的基于循环神经网络的深度学习网络架构，可以充分利用提取的粗粒度细粒度特征来估计健康指标（HI），其中 HI 能够指示滚轮的健康状态. 跟随那个，构建状态空间模型来描述 HI，并且可以通过将 HI 退化模型外推到预定义的故障阈值来估计 RUL 的概率分布。最后，通过使用从工业现场收集的数据，将其应用于热轧机以验证所提出方法的有效性，与其他一些流行的深度学习方法相比，相对较低的 RMSE 和 MAE 值证明了其优势。