论文标题
通过机器学习方法预测工业老化过程
Forecasting Industrial Aging Processes with Machine Learning Methods
论文作者
论文摘要
准确地预测工业老化过程可以提前进一步安排维护事件，从而确保工厂的经济高效和可靠的运营。到目前为止，这些降解过程通常通过机械或简单的经验预测模型来描述。在本文中，我们评估了更广泛的数据驱动模型，比较了一些传统的无状态模型（线性和内核脊回归，馈送前传神经网络）与更复杂的复发神经网络（Echo State Networks和LSTMS）。我们首先检查了具有已知动力学的合成数据集上的每个模型需要多少历史数据。接下来，对大型化学厂的现实数据进行测试。我们的结果表明，在较大的数据集中训练时，经常性模型即使在具有域移动的较小数据集上进行培训，并且在较小的模型上仅在较小的数据集上进行了相当的性能，也可以保持良好的性能。
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic or simple empirical prediction models. In this paper, we evaluate a wider range of data-driven models, comparing some traditional stateless models (linear and kernel ridge regression, feed-forward neural networks) to more complex recurrent neural networks (echo state networks and LSTMs). We first examine how much historical data is needed to train each of the models on a synthetic dataset with known dynamics. Next, the models are tested on real-world data from a large scale chemical plant. Our results show that recurrent models produce near perfect predictions when trained on larger datasets, and maintain a good performance even when trained on smaller datasets with domain shifts, while the simpler models only performed comparably on the smaller datasets.
