Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction

TL;DR

This paper introduces THGNN, a novel graph neural network model that captures fine-grained temporal, spatial, and heterogeneous sensor data relationships to improve remaining useful life prediction in industrial systems.

Contribution

The paper proposes THGNN, a model that effectively models temporal and spatial dependencies along with sensor heterogeneity using graph neural networks and FiLM, advancing RUL prediction accuracy.

Findings

Achieved up to 19.2% improvement in evaluation metrics.

Achieved up to 31.6% improvement in evaluation metrics.

Validated on the N-CMAPSS dataset with superior performance.

Abstract

Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time series sensory data from such systems, deep learning models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modelled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal graph, a coarse-grained approach that leads to loss of temporal information. Moreover, given the variety of heterogeneous sensors, it becomes vital that such inherent heterogeneity is leveraged for RUL prediction in temporal sensor graphs. To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN). Specifically, THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data in a fine-grained manner. Moreover, the model leverages Feature-wise Linear Modulation (FiLM) to address the diversity of sensor types, significantly improving the model's capacity to learn the heterogeneity in the data sources. Finally, we have validated the effectiveness of our approach through comprehensive experiments. Our empirical findings demonstrate significant advancements on the N-CMAPSS dataset, achieving improvements of up to 19.2% and 31.6% in terms of two different evaluation metrics over state-of-the-art methods.