The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Convolutional Transformer: An Enhanced Attention Mechanism Architecture for Remaining Useful Life Estimation of Bearings
Abstract
Nowadays, deep learning (DL) methods for prognostic and health management (PHM) have vastly broadened the scope of applications in this field. Numerous approaches based on deep neural networks have been presented and applied to the remaining useful life (RUL) estimation of bearings. However, few of these methods are yet fully competent for the task of extracting degradation-related information from raw signals both locally and globally. To fill this research gap, we proposed a novel convolutional Transformer (CoT) which combines the global context capturing of attention mechanism with the local dependencies modeling of convolutional operation. Specifically, we designed a multi-scale convolutional (MSC) module with Swish activation for Transformer architecture to embed local feature learning into global sequence modeling. Our CoT fuses intra-token convolution and inter-token self-attention operations to enable simultaneous extraction of local dependencies and global interactions from the raw temporal signal into a trainable class token. Then, an end-to-end RUL estimation framework based on CoT is presented, which provides a mapping from raw vibration signals to estimated RULs. Finally, comprehensive case studies, including comparative studies and ablation experiments, fully validate the effectiveness and advancements of our CoT-based RUL estimation method.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recent advancements have focused on developing sophisticated loss functions and regularization techniques, aimed at enhancing the performance and generalization capabilities of supervised learning models, ensuring they remain robust and effective across a wide range of tasks and data types [166], [167], [168].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [166]
Citations
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169
Conformer: Convolution-augmented Transformer for Speech Recognition
Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., et al. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-3015 · Cited by 3,150
Searching for activation functions
Ramachandran, P., Zoph, B., & Le, Q. V. (2017) · arXiv · doi:10.48550/arxiv.1710.05941 · Cited by 2,335
Attention Augmented Convolutional Networks
Bello, I., Zoph, B., Le, Q. V., Vaswani, A., & Shlens, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00338 · Cited by 1,347
Efficient transformers: A survey
Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023) · ACM Computing Surveys · doi:10.1145/3530811 · Cited by 1,267
PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel‐Morello, B., Noureddine Zerhouni, A., & Varnier, C. (2012) · HAL · Cited by 928
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733438 · Cited by 899
Flagged first, then by citations
Research works printed but not linked: 3
- Dosovitskiy, An image is worth $16\times16$ words: Transformers for image recognition at scale
- 10.1145/3474085.3475272 · DOI
- Liang, EViT: Expediting vision transformers via token reorganizations
Cited by
Sorted by citations
Items 101–120 of 120
Qiao, X., Jauw, V. L., Banda, T., & Lim, C. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5067077
Attention-Enhanced Deep Learning for Accurate Musculoskeletal X-Ray ClassificationPreprint
Alammar, Z., Alzubaidi, L., Zhang, J., Li, Y., & Gu, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5143442
Zhang, L., Ma, C., Wang, G., Cao, X., Zhang, J., Tian, S., & Wang, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5224523
LSGAN-Transformer life prediction method for rolling bearings under few samples
Meng, F., Deng, K., Wang, L., Cui, J., Qian, Y., Du, W., Gong, X., & Wang, L. (2025) · Journal of Vibroengineering · doi:10.21595/jve.2025.24741
Shi, Y., Yan, X., Zhang, Y., Yang, D., Song, Q., Zhang, W., & Deng, L. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2569783
Residual life prediction of progressive failure bearings based on NGO-AVMD hybrid domain features
Zhou, J. (2026) · Frontiers in Signal Processing · doi:10.3389/frsip.2025.1729918
Pi, D., & Hou, D. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3668353
Real-Time Visualization of Dynamic Feature Importance for Remaining Useful Life Prediction
Amraouy, E. H., Hammou, A., Yahyaouy, A., Chaoui, H., & Gualous, H. (2026) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418849
Li, C., Song, M., & Ma, Y. (2026) · Journal of Sustainable Forestry · doi:10.1080/10549811.2026.2646689
Su, B.-W., Chen, X.-P., Dai, Y.-H., Guan, T., & Li, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133757
Tang, Y., Ma, C., & Li, C. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6ac1
Wang, Y., Zhang, Y., Shi, H., Tao, Q., & Zhang, L. (2025) · International Conference on Mechanical Engineering and Aerospace Engineering (MEAE) · doi:10.1109/meae68077.2025.11557929
A Novel Uncertainty-Aware Pairwise Ranking Method for Remaining Useful Life Prediction
Shao, C., Ma, L., Wang, S. P., & Wang, S. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11559787
Prediction of bearing remaining life based on STFT-SWT and Transformer-BiLSTM
Wang, D., Yang, X., Liu, S., & Hou, R. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae8950
Gao, H., Feng, T., & GAO, X. (2026) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls71227.2026.11610565
Mu, L., Zhang, W., Liu, Z., & Chen, J. (2026) · International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) · doi:10.1109/aiea70743.2026.11632580
Zhang, Y. (2026) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat71030.2026.11682466
Pour, M. M., Ghasemzadeh, A., Bijarchi, M. A., & Shafii, M. B. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105232
Wu, J., Su, S., Li, H., & Wang, L. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261488245
Transformer-Based Fault Diagnosis Model for Cooperative Ground-Air Unmanned Formation Systems
Jia, H., Chen, Y., & Guo, X. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-30756-9_2
Metadata
- Authors
- Ding, Y., & Jia, M.
- Affiliations
- Southeast University (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2022
- Volume
- 71
- Pages
- 1-10
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W4285186957
- Cited by
- 120
- References
- 38
- Citations by year
- 2026: 18 · 2025: 39 · 2024: 39 · 2023: 21 · 2022: 3
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Foundation of China (52075095)
- Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX21_0082)
- 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 › Electrical and Electronic Engineering › Power Transformer Diagnostics and Insulation
- Keywords
- remaining useful life prediction, bearings, convolutional transformer, attention mechanism, multi-scale convolution, self-attention, vibration signals, degradation modeling, prognostics and health management, class token, local and global feature extraction
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
