# Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

arXiv: https://arxiv.org/abs/2006.16556 · HTML: https://mlsanity.com/paper/2006.16556/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions](https://mlsanity.com/paper/2203.06852/) (2022)
- [Graph Learning for Cognitive Digital Twins in Manufacturing Systems](https://mlsanity.com/paper/2109.08632/) (2021)
- [Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs](https://mlsanity.com/paper/2202.05517/) (2022)
- [Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimation](https://mlsanity.com/paper/2212.05772/) (2022)
- [Interaction models for remaining useful life estimation](https://mlsanity.com/paper/2301.05029/) (2023)

## Abstract (arXiv)

Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure high operational availability. We note that most complex machinery has a well-documented and readily accessible underlying structure capturing the inter-dependencies between sub-systems or modules. Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure. In this work, we propose to capture the structure of a complex equipment in the form of a graph, and use graph neural networks (GNNs) to model multi-sensor time-series data. Using remaining useful life estimation as an application task, we evaluate the advantage of incorporating the graph structure via GNNs on the publicly available turbofan engine benchmark dataset. We observe that the proposed GNN-based RUL estimation model compares favorably to several strong baselines from literature such as those based on RNNs and CNNs. Additionally, we observe that the learned network is able to focus on the module (node) with impending failure through a simple attention mechanism, potentially paving the way for actionable diagnosis.