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.

准确预测工业老化过程可以提前安排维护事件，从而确保工厂经济高效且可靠地运行。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，我们将一些传统的无状态模型（线性和内核岭回归，前馈神经网络）与更复杂的递归神经网络（回声状态网络和LSTM）进行了比较，从而评估了更广泛的数据驱动模型。我们首先检查需要多少历史数据才能在具有已知动态的合成数据集上训练每个模型。接下来，将对来自大型化工厂的真实数据进行测试。我们的结果表明，在较大的数据集上进行训练时，递归模型可以产生接近完美的预测，