Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks
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. The 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: We sincerely thank the reviewers for their valuable feedback and constructive suggestions. Following the reviewers' comments, we have revised the manuscript accordingly. The main updates are as follows:
1. **Validation on N-CMAPSS Dataset**
We have added validation results on the N-CMAPSS dataset. This includes updated data descriptions, experimental setups, detailed result analyses, and relevant figures and tables.
2. **Improved Description of MT-FGNE Framework**
The description of the proposed MT-FGNE framework has been refined to ensure clarity in its components. We also highlighted its distinctions from traditional ST-GNN approaches.
3. **Enhanced Ablation Study with Visualization**
We have expanded the ablation study with additional experiments and provided comprehensive visual analyses of the results.
We believe these improvements address the reviewers' concerns and enhance the overall quality of the manuscript. Thank you for your time and consideration.
Assigned Action Editor: ~Mingsheng_Long2
Submission Number: 3031
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