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Cited by 10Machine Fault Diagnosis TechniquesReliability and Maintenance OptimizationFault Detection and Control Systems

Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction

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Huang, Zhixin, He, Yujiang, Sick, Bernhard·2023·2023 International Conference on Computational Science and Computational Intelligence (CSCI)
DOI 10.1109/csci62032.2023.00022
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Abstract

Remaining useful life prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multi-head attention mechanisms for both spatio- temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the C-MAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.

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  • The modest improvement observed in models [99,100] is likely due to the noise reduction achieved by preprocessing.

    “The modest improvement observed in these models is likely due to the noise reduction achieved by preprocessing.”

    From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [100]

    The clustering-normalization section (page unavailable) describes noise from unified normalization and more consistent sensor scales after cluster normalization. The Evaluation Metrics section reports improved RUL prediction with that preprocessing, supporting the proposed explanation. It calls the improvement significant, however, and reports gains of up to 26% in RMSE and 27% in Score, so describing it as modest does not accurately reflect the reported results. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 27

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Cited by

Sorted by citations

Authors
Huang, Z., He, Y., & Sick, B.
Affiliations
University of Kassel (DE)
ORCID
Venue
2023 International Conference on Computational Science and Computational Intelligence (CSCI)
Publisher
IEEE
Published
2023-12-13
Pages
99-105
DOI
10.1109/csci62032.2023.00022
OpenAlex
W4400810695
Cited by
10
References
2
Citations by year
2026: 2 · 2025: 4 · 2024: 4
FWCI
4.28
Type
conference paper
Language
en
Open access
Licence
Locations
  • International Conference on Computational Science and Computational Intelligence (CSCI) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
remaining useful life prediction, spatio-temporal attention, graph neural networks, temporal convolutional network, multi-head attention, data normalization, operating conditions, C-MAPSS dataset, model explainability

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