Publication Date

10-1-2023

Document Type

Article

Publication Title

Advanced Engineering Informatics

Volume

58

DOI

10.1016/j.aei.2023.102143

Abstract

Graph Convolutional Networks (GCNs) have recently been used to predict the remaining useful life (RUL) of bearings due to its effectiveness in revealing correlations in condition monitoring data. However, traditional GCNs use a single graph only, either a temporal-correlated graph or a feature-correlated graph without considering both temporal and feature correlations of condition monitoring data. Additionally, traditional GCNs rely heavily on pre-defined graphs to aggregate correlated features. However, the topology of these pre-defined graphs may vary depending on a pre-defined threshold for cosine similarity or covariance which might affect prediction accuracy and robustness. To address these issues, we introduce a spectral graph convolutional operation that can handle both temporal-correlated and feature-correlated graphs, which allows one to consider both the temporal and feature correlations simultaneously. Moreover, we introduce a self-attention mechanism to construct the temporal-correlated and feature-correlated graphs automatically without defining a threshold. Such a mechanism allows the predictive model to learn graphs automatically during training so that the prediction accuracy and robustness can be significantly improved. The proposed method is demonstrated on two bearing datasets, and the experimental results have shown that it outperforms both traditional GCNs and other deep-learning methods in predicting RUL of bearings.

Keywords

Attention mechanism, Bearing, Deep learning, Graph convolutional network, Remaining useful life

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Department

Industrial and Systems Engineering

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