Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks

Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang

Abstract: Remaining useful life (RUL) prediction is crucial in predictive maintenance. Recently, deep learning forecasting methods, especially Spatio-Temporal Graph Neural Networks (ST-GNNs), have achieved remarkable performance in RUL prediction. Most existing ST-GNNs require searching for the graph structure before utilizing GNNs to learn spatial graph representation, and they necessitate a temporal model such as LSTM to leverage the temporal dependencies in a fixed lookback window. However, such an approach has several limitations. Firstly, it demands substantial computational resources to learn graph structures for the time series data. Secondly, independently learning spatial and temporal information disregards their inherent correlation, and thirdly, capturing information within a fixed lookback window ignores long-term dependencies across the entire time series. To mitigate the issues above, instead of treating the data within the lookback window as a sequence of graphs in ST-GNN methods, we regard it as a complete graph and employ a Fourier Graph Neural Network (FGN) to learn the spatiotemporal information within this graph in the frequency space. Additionally, we create training and test graphs with varying sizes of lookback windows, enabling the model to learn both short-term and long-term dependencies and provide multiple predictions for ensemble averaging. We also consider scenarios where sensor signals exhibit multiple operation conditions and design a sequence decomposition plugin to denoise input signals, aiming to enhance the performance of FGN. We evaluate the proposed model on two benchmark datasets, demonstrating its superior performance on the RUL prediction task compared to state-of-the-art approaches.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

The authors thank the reviewers and the Associate Editor for their valuable feedback. In response to the detailed comments regarding the writing and formatting of the paper, we have made the following improvements.

1. **Figure 1 Optimization**: The layout has been redesigned for better visual presentation.
2. **Citation Style**: All citations have been reformatted according to the journal guidelines.
3. **Language Enhancement**: Informal expressions have been replaced with more precise academic language.
4. **Mathematical Notation**: Equation notation and formatting have been standardized throughout the manuscript.

We believe these improvements address the reviewers' concerns and enhance the overall quality of the manuscript, and submit this Camera Ready Revision. Thank you for your time and consideration.

Assigned Action Editor: ~Mingsheng_Long2

Submission Number: 3031