Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Open accessAbstract
Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal graph, a coarse-grained approach that leads to a loss of temporal information. Moreover, given the variety of heterogeneous sensors, it becomes vital that such inherent heterogeneity is leveraged for RUL prediction in temporal sensor graphs. To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named temporal and heterogeneous graph neural networks (THGNNs). Specifically, THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data in a fine-grained manner. Moreover, the model leverages feature-wise linear modulation (FiLM) to address the diversity of sensor types, significantly improving the model's capacity to learn the heterogeneity in the data sources. Finally, we have validated the effectiveness of our approach through comprehensive experiments. Our empirical findings demonstrate significant advancements on the N-CMAPSS dataset, achieving improvements of up to 19.2% and 31.6% in terms of two different evaluation metrics over state-of-the-art methods.
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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 [99]
The Comparative Analysis (page unavailable) reports improved predictive performance for THGNN, so the paper supports an improvement in the model's results. The Datasets section (page unavailable) mentions preprocessing and normalization, but neither it nor the performance analysis attributes the improvement to noise reduction; the paper also describes the improvement as significant rather than modest. (LLM, conf=0.78)
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752 · see citing works
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Zhao, L. D., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2935152 · Cited by 3,358 · see citing works
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191 · see citing works
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Wu, Z., Pan, S., Long, G., Jiang, J. B., & Zhang, C. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/264 · Cited by 3,158 · see citing works
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490 · see citing works
Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G. (1971) · Biometrika · doi:10.1093/biomet/58.1.83 · Cited by 2,306 · see citing works
Conditional Prompt Learning for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01631 · Cited by 1,918 · see citing works
A Transformer-based Framework for Multivariate Time Series Representation Learning
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467401 · Cited by 1,278 · see citing works
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Fu, X., Zhang, J., Meng, Z., & King, I. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380297 · Cited by 1,056 · see citing works
Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life
Babu, G. S. D., Zhao, P., & Li, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32025-0_14 · Cited by 1,007 · see citing works
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582798 · Cited by 869 · see citing works
Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371845 · Cited by 660 · see citing works
Recurrent neural networks for remaining useful life estimation
Heimes, F. O. (2008) · International Conference on Prognostics and Health Management (PHM) · doi:10.1109/phm.2008.4711422 · Cited by 655 · see citing works
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., & Li, X. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2972443 · Cited by 622 · see citing works
Self-citationJin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567 · see citing works
Continuous-Time Dynamic Network Embeddings
Nguyen, G. H., Lee, J. B., Rossi, R. A., Ahmed, N. K., Koh, E., & Kim, S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191526 · Cited by 564 · see citing works
Dynamic Network Embedding by Modeling Triadic Closure Process
Zhou, L.-K., Yang, Y., Ren, X., Wu, F. Q., & Zhuang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11257 · Cited by 484 · see citing works
A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions
Huang, C., Huang, H., & Li, Y. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2891463 · Cited by 477 · see citing works
Long short-term memory for machine remaining life prediction
Zhang, J., Wang, P. E., Yan, R., & Gao, R. X. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.05.011 · Cited by 464 · see citing works
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Yu, P. S. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177455 · Cited by 456 · see citing works
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
Chao, M. A., Kulkarni, C. S., Goebel, K., & Fink, O. (2021) · Data · doi:10.3390/data6010005 · Cited by 345 · see citing works
Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit
Mo, Y., Wu, Q., Li, X., & Huang, B. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01750-x · Cited by 334 · see citing works
Embedding Temporal Network via Neighborhood Formation
Zuo, Y., Liu, G., Lin, H., Guo, J., Hu, X., & Wu, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220054 · Cited by 294 · see citing works
Miao, H., Li, B., Sun, C., & Liu, J. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2900295 · Cited by 293 · see citing works
Jia, Z., Lin, Y., Wang, J., Ning, X., He, Y., Zhou, R., Zhou, Y., & Lehman, L.-W. H. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3110665 · Cited by 272 · see citing works
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
Li, J., Li, X., & He, D. (2019) · IEEE Access · doi:10.1109/access.2019.2919566 · Cited by 257 · see citing works
Li, T., Zhao, Z., Sun, C., Yan, R., & Chen, X. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107878 · Cited by 255 · see citing works
Remaining Useful Life Prediction using Deep Learning Approaches: A Review
Wang, Y., Zhao, Y., & Addepalli, S. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.06.015 · Cited by 254 · see citing works
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Chen, L., Chen, D., Shang, Z., Wu, B., Zheng, C., Wen, B., & Zhang, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3268199 · Cited by 250 · see citing works
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding
Du, L., Wang, Y., Song, G., Lu, Z., & Wang, J. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/288 · Cited by 222 · see citing works
Wei, Y., Wu, D., & Terpenny, J. P. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.110010 · Cited by 159 · see citing works
Deep Dynamic Network Embedding for Link Prediction
Li, T., Zhang, J., Yu, P. S., Zhang, Y., & Yan, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2839770 · Cited by 141 · see citing works
Xu, Q. Y., Chen, Z., Wu, K., Wang, C., Wu, M., & Li, X. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3057030 · Cited by 136 · see citing works
Self-citationEvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T. B., & Leiserson, C. E. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5984 · Cited by 128 · see citing works
Wen, L., Li, X., & Gao, L. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3044808 · Cited by 128 · see citing works
Zhao, C., Huang, X., Li, Y., & Iqbal, M. Y. (2020) · Sensors · doi:10.3390/s20247109 · Cited by 128 · see citing works
Dynamic Knowledge Graph based Multi-Event Forecasting
Deng, S., Rangwala, H., & Ning, Y. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403209 · Cited by 114 · see citing works
TREND: TempoRal Event and Node Dynamics for Graph Representation Learning
Wen, Z., & Fang, Y. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512164 · Cited by 95 · see citing works
Self-citationDegradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning
Li, X., We, Z., Ma, H., Luo, Z., & Li, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070840 · Cited by 91 · see citing works
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery
Jayasinghe, L., Samarasinghe, T., Yuen, C., Low, J., & Ge, S. S. (2019) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2019.8754956 · Cited by 87 · see citing works
Fu, S., Zhong, S., Lin, L., & Zhao, M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084249 · Cited by 82 · see citing works
A new ensemble residual convolutional neural network for remaining useful life estimation
Wen, L., Dong, Y., & Gao, L. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019040 · Cited by 79 · see citing works
A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction
Gao, Y., Wen, Y., & Wu, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2977132 · Cited by 76 · see citing works
Wang, M., Li, Y., Zhang, Y., & Jia, L. (2020) · Aerospace Systems · doi:10.1007/s42401-020-00070-x · Cited by 70 · see citing works
Local–Global Correlation Fusion-Based Graph Neural Network for Remaining Useful Life Prediction
Wang, Y., Wu, M., Jin, R., Li, X., Xie, L., & Chen, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3330487 · Cited by 65 · see citing works
Self-citationRen, L., Liu, Y., Huang, D., Huang, K., & Yang, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3136768 · Cited by 65 · see citing works
Generative adversarial networks based remaining useful life estimation for IIoT
Behera, S., & Misra, R. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107195 · Cited by 64 · see citing works
Adaptive Spatio-Temporal Graph Information Fusion for Remaining Useful Life Prediction
Zhang, Y., Li, Y., Wang, Y., Yang, Y., & Wei, X. H. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3136622 · Cited by 55 · see citing works
Jiang, Y., Xia, T., Wang, D., Fang, X., & Xi, L. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3154789 · Cited by 55 · see citing works
Song, W., Shen, W., Gao, L., & Li, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3132989 · Cited by 50 · see citing works
Dual Learning-Based Graph Neural Network for Remote Sensing Image Super-Resolution
Liu, Z., Feng, R., Wang, L., Han, W., & Zeng, T. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3199750 · Cited by 44 · see citing works
Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings
Wang, X., Wang, T., Ming, A., Han, Q., Chu, F., Zhang, W., & Li, A. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00576-1 · Cited by 38 · see citing works
Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction
Fu, J., Yang, F., Dang, Y., Liu, X., & Yin, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3277476 · Cited by 37 · see citing works
Graph Deep Learning for Time Series Forecasting
Cini, A., Marisca, I., Zambon, D., & Alippi, C. (2025) · ACM Computing Surveys · doi:10.1145/3742784 · Cited by 35 · see citing works
Meta-Inductive Node Classification across Graphs
Wen, Z., Fang, Y., & Liu, Z. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462915 · Cited by 34 · see citing works
Self-citationHong, C. W., Lee, K., Ko, M.-S., Kim, J.-K., Oh, K., & Hur, K. (2020) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp48618.2020.00-98 · Cited by 23 · see citing works
Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction
Mo, H., & Iacca, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02462-7_13 · Cited by 21 · see citing works
Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction
Zhang, Y., Zhou, W., Huang, J., Jin, X., & Xiao, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3309395 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 12
- P. Goyal, N. Kamra, X. He, and Y. Liu, "Dyngem: Deep embedding method for dynamic graphs," arXiv preprint arXiv:1805.11273, 2018. · arXiv
- A. Sankar, Y. Wu, L. Gou, W. Zhang, and H. Yang, "Dysat: Deep neural representation learning on dynamic graphs via self-attention networks," in Proceedings of the 13th international conference on web search and data mining, 2020, pp. 519-527.
- D. Xu, C. Ruan, E. Korpeoglu, S. Kumar, and K. Achan, "Inductive repre- sentation learning on temporal graphs," arXiv preprint arXiv:2002.07962, 2020. · arXiv
- H. Mei and J. M. Eisner, "The neural hawkes process: A neurally self- modulating multivariate point process," NeurIPS, vol. 30, 2017.
- H. Wu, J. Xu, J. Wang, and M. Long, "Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting," NeurIPS, vol. 34, pp. 22 419-22 430, 2021.
- L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, "Adaptive graph convolutional recurrent network for traffic forecasting," ArXiv, 2020.
- L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, "T-gcn: A temporal graph convolutional network for traffic prediction," IEEE TITS, vol. 21, pp. 3848-3858, 2018.
- D. Ha, A. M. Dai, and Q. V. Le, "Hypernetworks," in ICLR, 2017.
- K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, "Show, attend and tell: Neural image caption generation with visual attention," in International conference on machine learning. PMLR, 2015, pp. 2048-2057.
- Y. Zhang and J. Yan, "Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting," in ICLR, 2022.
- T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, and J. Choo, "Reversible instance normalization for accurate time-series forecasting against distribution shift," in ICLR, 2022.
- Zhenghua Chen received the B.Eng. degree in mechatronics engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2011, and Ph.D. degree in electrical and electronic engineering from Nanyang Technological University (NTU), Singapore, in 2017. He has been working at NTU as a research fellow. Currently, he is a scientist at Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore. He has won several competitive awards, such as First Place Winner for CVPR 2021 UG2+ Challenge, A*STAR Career Development Award, First Runner-Up Award for Grand Challenge at IEEE VCIP 2020, Finalist Academic Paper Award at IEEE ICPHM 2020, etc. He serves as Associate Editor for Elsevier Neurocomputing and Guest Editor for IEEE Transactions on Emerging Topics in Computational Intelligence. He is currently the Vice Chair of IEEE Sensors Council Singapore Chapter and IEEE Senior Member. His research interests include smart sensing, data analytics, machine learning, transfer learning and related applications.
Cited by
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Wang, Y., Wu, M., Li, X., Xie, L., & Chen, Z. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112449 · Cited by 91 · see citing works
Self-citationZhou, M., Li, Y., Cao, Y., Ma, X., & Xu, Z. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110685 · Cited by 58 · see citing works
Elsherif, S. M., Hafiz, B., Makhlouf, M. A., & Hassan, O. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-09155-z · Cited by 50 · see citing works
Özcan, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-23473-2 · Cited by 22 · see citing works
A cycle-aware and physics-informed framework for battery remaining useful life prediction
Chen, Y., Wu, Y., & Li, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-28505-5 · Cited by 16 · see citing works
Spatial-temporal graph hybrid neural network for remaining useful life prediction of aero-engines
Ren, Y., Meng, Z., & Li, J. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112433 · Cited by 13 · see citing works
Cui, W., Zhang, S., Sun, T., & Wang, R. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3626358 · Cited by 6 · see citing works
Kong, Q., Rong, Y., Wang, G., Xu, Z.-Q., Zhang, Q., Guan, Z.-R., & Fan, Y.-H. (2025) · Processes · doi:10.3390/pr13113520 · Cited by 5 · see citing works
Wu, X., Liu, Z., & Wang, L. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae26bb · Cited by 5 · see citing works
Remaining useful life prediction based on degradation-aware dynamic graph representation learning
Li, Q., Xiong, X., & Ren, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113140 · Cited by 4 · see citing works
Graph-Attention Diffusion for Enhanced Multivariate Time-Series Anomaly Detection
Lanko, V., & Makarov, I. (2024) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2024.3501014 · Cited by 3 · see citing works
Jun, S., Lee, C., Youn, S. J., & Lee, C. (2025) · Sustainability · doi:10.3390/su172310507 · Cited by 3 · see citing works
Knowledge-guided RUL prediction for multi-sensor devices under a data-model interactive framework
Li, H., Liu, X., Zhang, Z., & Si, X. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114203 · Cited by 3 · see citing works
Xu, L., Qu, Z., & Arebi, P. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01422-8 · Cited by 2 · see citing works
Baz, M. (2024) · Mathematics · doi:10.3390/math12213320 · Cited by 1 · see citing works
Synergistic Mamba-attention gated fusion for remaining useful life prediction of aircraft engines
Jiao, Z., & He, Q. (2026) · Measurement · doi:10.1016/j.measurement.2026.121862 · Cited by 1 · see citing works
Butorovа, A. S., Bobakov, V. S., Sergeev, A., Владимирович, Р. М., Борисов, В., Hushchyn, M., Baglaeva, E., Buevich, A., & Shichkin, A. V. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02411-x · Cited by 1 · see citing works
li, H., Wang, J., Wen, Y., Liu, H., & Ju, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.17.694956
Zhang, Q., & Gao, M. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08294-y
Zhao, Y., Cheng, J., & Song, P. (2025) · International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT) · doi:10.1109/memat68155.2025.11434100
Yu, J., & Zhao, J. (2026) · International Conference on Communication Networks and Machine Learning (CNML) · doi:10.1109/cnml68938.2026.11452405
A Brain-Inspired Model for Efficient Graph Learning in Human Cyber Physical Networks
Gish, C. J., Villalba-Díez, J., & Ordieres-Meré, J. B. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10574-x
MUHAMMAD, B. M., & Usman, U. U. U. (2026) · International Journal of Science for Global Sustainability · doi:10.57233/ijsgs.v12i1.1062
LDGC3: Learnable deep graph contrastive clustering with triple cluster-structure awareness
Li, Y., Li, X., Wang, H., & Zu, B. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133165
Rafi, M. S. A., & Hasan, M. K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.122771
Zhou, M., Li, Y., Liu, W. B., Ma, X., & Xu, Z. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113138
Continuous degradation representation learning for remaining useful life prediction
Li, W., Ning, R., He, S., Li, B., & Zhang, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-75502-3
Zhao, M., Yang, C., Wang, X., Tian, J., Zhu, Y., Ouyang, H., Duan, H., Ma, F., Tian, F., & Wang, H. (2026) · doi:10.2196/preprints.106158
Liu, C., Li, D., & Xu, X. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3719757
Suhasini, K. C., & Lakshmi, T. S. (2026) · International Conference on Intelligent and Sustainable AI Systems (ICOSAAS) · doi:10.1109/icosaas68663.2026.11648482
Zeng, Z., Zhang, Z., Li, T., Feng, L., Si, X., & Zhang, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115984
Wang, H., Sun, Y., Liu, H., Li, J., Duan, H., & Wang, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7371094
A Review of Data-Driven and Hybrid Approaches to Predictive Maintenance for Civil Aircraft
李, 伟. (2026) · Computer Science and Application · doi:10.12677/csa.2026.169288
Fei, Q., & Li, F. (2026) · Engineering Research Express · doi:10.1088/2631-8695/aeab2d
Peng, P., Fan, X., Lin, B., Sun, X., Yin, Z., & Guo, Z. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113421
Intelligent spiking GAN-residual framework for accurate SOH, and RUL prediction in EV battery
Geevarghese, P. A., & Suresh, L. P. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111631
Liu, W. V., Zhou, M., Zheng, R., & Cheng, B.-Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113711
- Authors
- Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M.
- Affiliations
- Singapore Management University (SG)
- Singapore University of Technology and Design (SG)
- Agency for Science, Technology and Research (SG)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2025-11
- Volume
- 36
- Issue
- 11
- Pages
- 19748-19761
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W4412837103
- Cited by
- 34
- References
- 61
- Citations by year
- 2026: 21 · 2025: 11 · 2024: 2
- FWCI
- 11.75
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Agency for Science, Technology and Research (A*STAR) through its AME Programmatic Funds (A20H6b0151)
- Ministry of Education, Singapore, through its Academic Research Fund Tier 2 (T2EP20122-0041)
- 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, temporal graph neural networks, heterogeneous graph neural networks, prognostics and health management, sensor time series, spatial dependence, feature-wise linear modulation, N-CMAPSS
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