Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Comparing the results of [102] with those of [97,101], the model of [102] shows some improvement, which could be due to the parallel approach in that work.
“Comparing the results of [102] with those of [97,101], it can be seen that the model of [102] shows some improvement compared with these models, which could be due to the parallel approach in this work.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [101]
The architecture of [97] follows the design approach of [101] by using GAT to extract spatial features and multi-head self-attention to extract temporal features, with the exception that in [97] an adaptive embedding vector is added to each node within the GCN.
“A look at the architectures of this model shows that it follows the design approach of [101] by using GAT to extract the spatial features and multi-head self-attention to extract the temporal features, with the exception that in [97], an adaptive embedding vector is added to each node within the GCN.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [101]
The use of the TVG algorithm to map the MTS dataset into graphs eliminates the need to censor or truncate some parts of the MTS relationships, to rely on simplified metrics such as cosine similarity, or to patch the MTS.
“In particular, the use of the TVG algorithm to map the MTS dataset into graphs eliminates the need to censor or truncate some parts of the MTS relationships, as is the case with the constrained correlation approach used in [102]; to rely on simplified metrics such as cosine similarity, as is the case in [101]; or to patch the MTS, as is the case in [96].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [101]
One possible reason for the modest performance of the model in [101] is the use of cosine similarity to define the connectivity of the graphs representing the MTS.
“One of the possible reasons for the modest performance of this model could be the use of cosine similarity to define the connectivity of the graphs representing the MTS.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [101]
The model proposed in [101] shows the lowest performance on the supervised learning tasks compared to the other models.
“For example, the model proposed in [101] shows the lowest performance on the supervised learning tasks compared to the others.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [101]
Cites
Damage propagation modeling for aircraft engine run-to-failure simulation
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008) · 2008 International Conference on Prognostics and Health Management · doi:10.1109/phm.2008.4711414 · Cited by 1,954 · see citing works
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Baz, M. (2024) · Mathematics · doi:10.3390/math12213320 · Cited by 1 · see citing works
- Authors
- Liang, P., Li, Y., Wang, B., Yuan, X., & Zhang, L.
- Affiliations
- Yanshan University (CN)
- Hebei Agricultural University (CN)
- Corresponding author
- Zhang, Lijie
- Venue
- International Journal of Fatigue
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 174
- Pages
- 107722
- ISSN
- 0142-1123 (ISSN-L)
- OpenAlex
- W4377246612
- Cited by
- 101
- References
- 1
- Citations by year
- 2026: 31 · 2025: 43 · 2024: 23 · 2023: 4
- FWCI
- 10.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Fatigue (publishedVersion)
- Funders
- Natural Science Foundation of Hebei Province (E2022203022)
- 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, graph attention networks, machine condition monitoring, spatial correlation, temporal correlation, self-attention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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