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

TL;DR

This paper introduces a graph neural network approach for equipment health monitoring, leveraging the machinery's structure to improve remaining useful life estimation and enable more interpretable diagnostics.

Contribution

It proposes a novel GNN-based model that explicitly incorporates equipment structure, outperforming traditional RNN and CNN models in RUL estimation tasks.

Findings

GNN-based model outperforms RNN and CNN baselines.

The model can focus on failing modules via attention.

Provides interpretable insights into failure modes.

Abstract

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.