# Forecasting Industrial Aging Processes with Machine Learning Methods

arXiv: https://arxiv.org/abs/2002.01768 · HTML: https://mlsanity.com/paper/2002.01768/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery](https://mlsanity.com/paper/2407.18935/) (2024)
- [Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points](https://mlsanity.com/paper/2307.13621/) (2023)

## Abstract (arXiv)

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.