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.
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“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]
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Attention Augmented Convolutional Networks
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- 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
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Yan, S., Shao, H., Wang, J., Zheng, X., & Liu, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121338 · Cited by 409
Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023) · Sensors · doi:10.3390/s23031305 · Cited by 268
Han, S.-Y., Shao, H., Cheng, J., Yang, X., & Cai, B. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3199985 · Cited by 224
Zhang, Y., Su, C., Wu, J., Liu, H., & Xie, M. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109662 · Cited by 155
Li, X., Xiao, S., Li, Q., Zhu, L., Wang, T., & Chu, F. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111122 · Cited by 122
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
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