Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings

Attention Is All You Need

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

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

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 · see citing works

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 · see citing works

Searching for activation functions

Ramachandran, P., Zoph, B., & Le, Q. V. (2017) · arXiv · doi:10.48550/arxiv.1710.05941 · Cited by 2,335 · see citing works

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 1,320 · see citing works

Efficient transformers: A survey

Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023) · ACM Computing Surveys · doi:10.1145/3530811 · Cited by 1,267 · see citing works

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 · see citing works

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 881 · see citing works

Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.05.063 · Cited by 789 · see citing works

Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

Zhu, J., Chen, N., & Peng, W. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2844856 · Cited by 708 · see citing works

Pay Less Attention with Lightweight and Dynamic ConvolutionsPreprint

Wu, F., Fan, A., Baevski, A., Dauphin, Y., & Auli, M. (2019) · International Conference on Learning Representations · doi:10.48550/arxiv.1901.10430 · Cited by 669 · see citing works

A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings

Ding, Y., Jia, M., Miao, Q., & Cao, Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108616 · Cited by 559 · see citing works

Visual Transformers: Token-based Image Representation and Processing for Computer VisionPreprint

Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03677 · Cited by 529 · see citing works

Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process

Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.01.006 · Cited by 498 · see citing works

Deep separable convolutional network for remaining useful life prediction of machinery

Wang, B., Lei, Y., Li, N., & Yan, T. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106330 · Cited by 415 · see citing works

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

Chen, Y., Peng, G., Zhu, Z., & Li, S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105919 · Cited by 390 · see citing works

Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes

Zhao, M., Kang, M., Tang, B., & Pecht, M. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2762639 · Cited by 380 · see citing works

Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment

Liao, L., Jin, W., & Pavel, R. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2586442 · Cited by 285 · see citing works

Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network

Ren, L., Sun, Y., Wang, H. H., & Li, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2804930 · Cited by 283 · see citing works

Lite Transformer with Long-Short Range AttentionPreprint

Wu, Z., Liu, Z., Lin, J., Lin, Y., & Han, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11886 · Cited by 279 · see citing works

A BiGRU method for remaining useful life prediction of machinery

She, D., & Jia, M. (2020) · Measurement · doi:10.1016/j.measurement.2020.108277 · Cited by 248 · see citing works

A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings

Cheng, C., Ma, G., Zhang, Y., Sun, M., Teng, F., Ding, H., & Yuan, Y. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.2971503 · Cited by 238 · see citing works

Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings

Zhao, H., Liu, H., Jin, Y. F., Dang, X., & Deng, W. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3059500 · Cited by 197 · see citing works

Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine

He, Z., Shao, H., Ding, Z., Jiang, H., & Cheng, J. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3050382 · Cited by 166 · see citing works

Weighted Transformer Network for Machine TranslationPreprint

Ahmed, K., Keskar, N., & Socher, R. (2017) · arXiv.org · doi:10.48550/arxiv.1711.02132 · Cited by 143 · see citing works

A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case

Li, X., Cheng, J., Shao, H., Liu, K., & Cai, B. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3125385 · Cited by 135 · see citing works

Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy

Chen, Z., Li, Y., Xia, T., & Pan, E. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.09.002 · Cited by 131 · see citing works

Token shift transformer for video classification

ZHANG Hao, Hao, Y., & Ngo, C. (2021) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 121 · see citing works

Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals

Chao, Q., Gao, H., Tao, J., Wang, Y., Zhou, J. H., & Liu, C. (2021) · Science China Technological Sciences · doi:10.1007/s11431-021-1904-7 · Cited by 100 · see citing works

Remaining Useful Life Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation

Ding, Y., Jia, M., & Cao, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3076567 · Cited by 92 · see citing works

Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain

Tang, G., Wang, Y., Huang, Y., Liu, N., & He, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2965634 · Cited by 78 · see citing works

An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification

Jin, C., & Chen, X. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114570 · Cited by 69 · see citing works

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Shao, H., Li, W., Xia, M., Zhang, Y., Shen, C., Williams, D., Kennedy, A. R., & de Silva, C. W. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3111977 · Cited by 64 · see citing works

A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

Alexakos, C. T., Karnavas, Y. L., Drakaki, M., & Tziafettas, I. A. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3010011 · Cited by 62 · see citing works

A Convolutional Transformer Architecture for Remaining Useful Life Estimation

Ding, Y., & Jia, M. (2021) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-nanjing52125.2021.9612814 · Cited by 21 · see citing works

A Transformer Model-Based Approach to Bearing Fault Diagnosis

Bao, Z., Du, J., Zhang, W., Wang, J., Qiu, T., & Cao, Y. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-5940-9_5 · Cited by 19 · see citing works

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

LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

Yan, S., Shao, H., Wang, J., Zheng, X., & Liu, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121338 · Cited by 395 · see citing works

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023) · Sensors · doi:10.3390/s23031305 · Cited by 230 · see citing works

Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information

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 221 · see citing works

Trend-augmented and temporal-featured Transformer network with multi-sensor signals for remaining useful life prediction

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 · see citing works

The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy

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 · see citing works

An Adaptive Domain Adaptation Method for Rolling Bearings’ Fault Diagnosis Fusing Deep Convolution and Self-Attention Networks

Yu, X. L., Wang, Y., Liang, Z., Shao, H., Yu, K., & Yu, W. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3246494 · Cited by 105 · see citing works

From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works

ICoT-GAN: Integrated Convolutional Transformer GAN for Rolling Bearings Fault Diagnosis Under Limited Data Condition

Gao, H., Zhang, X., Gao, X., Li, F., & Han, H. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3271729 · Cited by 84 · see citing works

Auto-Embedding Transformer for Interpretable Few-Shot Fault Diagnosis of Rolling Bearings

Wang, G., Liu, D., & Cui, L. (2023) · IEEE Transactions on Reliability · doi:10.1109/tr.2023.3328597 · Cited by 70 · see citing works

MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion

Yu, Y., Karimi, H. R., Gelman, L., & Çetin, A. E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126947 · Cited by 70 · see citing works

A weighted time embedding transformer network for remaining useful life prediction of rolling bearing

Zhang, M., He, C., Huang, C., & Yang, J. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110399 · Cited by 68 · see citing works

Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction

Nguyen, H. D., Long, N. H., Ha, K. N., Hoang, N. V., Hương, T. T., & Tran, K. P. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103888 · Cited by 61 · see citing works

Bayesian large-kernel attention network for bearing remaining useful life prediction and uncertainty quantification

Wang, L., Cao, H., Ye, Z., & Xu, H. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109421 · Cited by 61 · see citing works

Extended attention signal transformer with adaptive class imbalance loss for Long-tailed intelligent fault diagnosis of rotating machinery

Chang, S., Wang, L., Shi, M., Zhang, J., Li, Y., & Cui, L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102436 · Cited by 52 · see citing works

The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions

Yang, T., Li, G., Li, K., Li, X., & Han, Q. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102558 · Cited by 50 · see citing works

Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis

Gao, D., Huang, K., Zhu, Y., Zhu, L., Yan, K. L., Ren, Z., & Soares, C. G. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109746 · Cited by 49 · see citing works

Conditional variational transformer for bearing remaining useful life prediction

Wei, Y., & Wu, D. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102247 · Cited by 47 · see citing works

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan, Z., Li, W., & Chang, K. (2024) · Sensors · doi:10.3390/s24030824 · Cited by 47 · see citing works

A trustworthy lightweight multi-expert wavelet transformer for rotating machinery fault diagnosis

Dong, Y., Jiang, H., Mu, M., & Wang, X. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112945 · Cited by 47 · see citing works

Local Enhancing Transformer With Temporal Convolutional Attention Mechanism for Bearings Remaining Useful Life Prediction

Peng, H., Jiang, B., Mao, Z., & Liu, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3291787 · Cited by 44 · see citing works

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Song, L., Lin, T., Jin, Y., Zhao, S., Li, Y., & Wang, H. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad5223 · Cited by 41 · see citing works

Multivariate time series generation based on dual-channel Transformer conditional GAN for industrial remaining useful life prediction

Zhang, Z., Gao, H., Sun, W., Song, W. W., & Li, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112749 · Cited by 41 · see citing works

Systematic Review of Bearing Component Failure: Strategies for Diagnosis and Prognosis in Rotating Machinery

Raj, K. K., Kumar, S., & Kumar, R. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09866-x · Cited by 39 · see citing works

Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions

Li, Y., Li, J., Wang, H., Liu, C., & Tan, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109748 · Cited by 38 · see citing works

Transformer-based novel framework for remaining useful life prediction of lubricant in operational rolling bearings

Kim, S.-H., Seo, Y.-H., & Park, J. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110377 · Cited by 33 · see citing works

A customized dual-transformer framework for remaining useful life prediction of mechanical systems with degraded state

Gao, Z., Jiang, W., Wu, J., Wang, Y., & Zhu, H. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112611 · Cited by 31 · see citing works

MMFSL: A Novel Multimodal Few-Shot Learning Framework for Fault Diagnosis of Industrial Bearings

Cheng, L., An, Z., Guo, Y., Ren, M., Yang, Z., & McLoone, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3289549 · Cited by 30 · see citing works

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Adekunle, A. A., Fofana, I., Picher, P., Rodriguez-Celis, E. M., & Arroyo-Fernàndez, O. H. (2024) · IEEE Access · doi:10.1109/access.2024.3391823 · Cited by 29 · see citing works

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

Deng, L., Li, W., & Yan, X. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2385074 · Cited by 28 · see citing works

Method to enhance time series rolling fault prediction by deep fast Fourier convolution

Yu, P., Ping, M., Ma, J., & Cao, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.114177 · Cited by 27 · see citing works

Remaining Useful Life Prediction for Bearing Based on Coupled Diffusion Process and Temporal Attention

Lu, Y., Tang, D., Zhu, D., Gao, Q., Zhao, D., & Lyu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3366270 · Cited by 25 · see citing works

Degradation-Aware Remaining Useful Life Prediction of Industrial Robot via Multiscale Temporal Memory Transformer Framework

Gao, Z., Wang, C., Wu, J., Wang, Y., Jiang, W., & Dai, T. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111176 · Cited by 23 · see citing works

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-Scale Attention Residual Network

Lin, S., Jun, W., Wang, L., Chen, G., Shi, Y., & Liu, Z. (2023) · Entropy · doi:10.3390/e25050798 · Cited by 22 · see citing works

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Fan, Z., Li, W., & Chang, K. (2023) · Mathematics · doi:10.3390/math11244972 · Cited by 22 · see citing works

A bearing RUL prediction approach of vibration fault signal denoise modeling with Gate-CNN and Conv-transformer encoder

Huang, P., Wang, Y., Gu, Y., & Qiu, G. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad2cd9 · Cited by 21 · see citing works

Global and local information integrated network for remaining useful life prediction

Chen, Z., Jin, X., Kong, Z., Wang, F., & Xu, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106956 · Cited by 20 · see citing works

WTFormer: RUL prediction method guided by trainable wavelet transform embedding and lagged penalty loss

Yang, Q., Tang, B., Deng, L., Zhu, P., & Ming, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102710 · Cited by 19 · see citing works

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Peng, H., Mao, Z., Jiang, B., & Cheng, Y. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3408141 · Cited by 19 · see citing works

Continuous Remaining Useful Life Prediction by Self-Guided Attention Convolutional Neural Network and Memory Consciousness Adjustment

Zhou, J., Qi, J., Chen, D., & Qin, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421673 · Cited by 20 · see citing works

Deep learning-based time series forecasting for bearing remaining useful life: Recent advances, hybrid architectures, and targeted enhancements

Tang, Z., Yang, D., Tan, L., & Zeng, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112457 · Cited by 17 · see citing works

A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint

Zhou, J., & Qin, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3462723 · Cited by 19 · see citing works

SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

Wang, H., Hua, Z., Li, H., & Ju, X. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.058785 · Cited by 18 · see citing works

Bearing Prognostics Using the PRONOSTIA Data: A Comparative Study

Dhungana, H., Rykkje, T. R., & Lundervold, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3551772 · Cited by 16 · see citing works

You can be more trustworthy: A feature fusion reinforcement network for credible anti-noise fault diagnosis

Wei, Y., Peng, H., Rong, M., Gu, X., & Chen, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103056 · Cited by 15 · see citing works

Multibranch Attention Graph Convolutional Networks for 3-D Human Pose Estimation

Yin, Y., Liu, M., Zhu, Q., Zhang, S., Hussien, N. A., & Fan, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3292957 · Cited by 15 · see citing works

Dynamic Self-Learning Neural Network and Its Application for Rotating Equipment RUL Prediction

Xiang, S., Zheng, X. W., Miao, J., Qin, Y., Li, P., Hou, J., & Илолов, М. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3520235 · Cited by 14 · see citing works

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Zhu, D., Lyu, J., Gao, Q., Lu, Y., & Zhao, D. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad214d · Cited by 12 · see citing works

Progressive hybrid hypergraph attention network with channel information fusion for remaining useful life prediction of rolling bearings

Zhang, Y., Su, C., He, X., Tang, J., Xie, M., & Liu, H. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112987 · Cited by 12 · see citing works

A comprehensive review on generative artificial intelligence models for fault diagnostics and prognostics of rotating machineries

Mian, T., & Kundu, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113594 · Cited by 11 · see citing works

Remaining useful life prediction method of bearings based on the interactive learning strategy

Wang, H., An, J., Yang, J., Xu, S., Wang, Z., Cao, Y., & Yuan, W. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109853 · Cited by 11 · see citing works

MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction

Rahman, M., Hossain, M. S., Rozario, U., Roy, S., Mridha, M. F., & Dey, N. (2025) · IEEE Access · doi:10.1109/access.2025.3586978 · Cited by 18 · see citing works

Data-Driven Soft Sensor for Hot Strip Mill Process Based on Multiscale Information-Fusion Attention Network

Xue, J., Li, K., Zhang, T., & Ye, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3450083 · Cited by 9 · see citing works

NPFormer: Interpretable rotating machinery fault diagnosis architecture design under heavy noise operating scenarios

Liu, H., Sun, Y., Wang, X., Wu, H., & Wang, H. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111878 · Cited by 9 · see citing works

Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation

Wang, Y., & Huang, Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.115714 · Cited by 9 · see citing works

A Novel Dual-Branch Transformer With Gated Cross Attention for Remaining Useful Life Prediction of Bearings

Cui, J., Ji, J., Zhang, T., Cao, L., Chen, Z., & Ni, Q. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3485918 · Cited by 9 · see citing works

Multi-Scale and Multi-Branch Transformer Network for Remaining Useful Life Prediction in Ion Mill Etching Process

Yuan, Z., & Wang, R. (2023) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2023.3324057 · Cited by 9 · see citing works

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art

Mogal, S. P., Bhandare, R. V., Phalle, V. M., & Kushare, P. B. (2024) · Tribologia - Finnish Journal of Tribology · doi:10.30678/fjt.141503 · Cited by 8 · see citing works

Remaining useful life prognosis of vacuum circuit breaker mechanical performance via vibration signal feature fusion

Zhao, H., Qian, Y., & Li, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adec0c · Cited by 8 · see citing works

A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

Liu, G., Zhang, C., Xu, S., Zhang, J., & Wu, L. (2024) · IEEE Access · doi:10.1109/access.2024.3399393 · Cited by 8 · see citing works

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

Wu, T., & Chen, T. (2023) · Sensors · doi:10.3390/s23041922 · Cited by 7 · see citing works

A Survey on Data Mining for Data-Driven Industrial Assets Maintenance

Coronel, E., Barán, B., & Gardel-Sotomayor, P. E. (2025) · Technologies · doi:10.3390/technologies13020067 · Cited by 7 · see citing works

A new dual-channel transformer-based network for remaining useful life prediction

Yang, K., Wei, Y., Ma, Y., Huang, L., Tang, Q., & Li, Z. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9e12 · Cited by 7 · see citing works

Trend-Enhanced Variate Transformer for Vessel Trajectory Prediction by Exploiting Short-Term Behavior Distribution Differences at Intersections

Deng, C., Wang, S., Li, J., Liu, J., Li, H., Zhao, Z., Guo, Y., & Song, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3552875 · Cited by 7 · see citing works

PMBCT: The Probabilistic Multiscale Bayesian Convolutional Transformer for Trustworthy Remaining Useful Life Prediction

Peng, H., Mao, Z., & Jiang, B. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3427797 · Cited by 6 · see citing works

Trend contrast features-based bearing remaining useful life prediction method

Zhu, Z., Lv, Z., & Xie, T. (2025) · Control Engineering Practice · doi:10.1016/j.conengprac.2025.106358 · Cited by 6 · see citing works

Data-driven wide-load modeling and electricity-heat coordinated control for the supercritical combined heat and power unit

Hou, G., Ye, L., & Cao, H. (2025) · Energy · doi:10.1016/j.energy.2025.137050 · Cited by 6 · see citing works

Remaining Useful Life Prediction Across Conditions Based on a Health Indicator-Weighted Subdomain Alignment Network

Xu, Z.-Q. J., Chow, C. W. K., Rahman, M. M., Rameezdeen, R., & Law, Y. W. (2025) · Sensors · doi:10.3390/s25154536 · Cited by 6 · see citing works

Frequency-aware transformer fusion for intelligent fault diagnosis of rolling bearings under variable operating conditions

Shahzad, A., Li, A., Liu, F., Jiang, M., Zhang, F., & Jia, Y. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae2949 · Cited by 6 · see citing works

Cognitive Data Fusing for Internet of Things Based on Ensemble Learning and Federated Learning

Gao, Z., Liu, S., & Zhang, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3377221 · Cited by 6 · see citing works

Advanced framework for intelligent fault diagnosis in rotary machinery with out-of-distribution recognition

Wang, T., Chen, X., Xie, J., Li, Y., Pan, T., & Yang, B. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adaa90 · Cited by 6 · see citing works

PMCFormer: a lightweight bearing fault diagnosis framework using multiscale partial convolution and pooling agent self-attention

Chen, Y., Yue, J., & Liu, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adbf8a · Cited by 6 · see citing works

VSAT: Variational Spectrum-Attention Transformer for Fault Diagnosis of Rotary Machine Monitoring

Bao, Y., Xie, Z., Li, Y., Li, Y., & Wang, Z. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3572472 · Cited by 5 · see citing works

A transformer-based framework with historical data fusion for RUL prediction

Jiang, L. Y., Zhang, X., Cao, H., & Zhang, Y. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae09c2 · Cited by 5 · see citing works

A Review on the Remaining Useful Life Prediction of Rotating Machinery Based on Deep Learning

Wang, Q., Li, Y., Xiong, J., Dong, X., Wu, Y., Huang, R., Zhu, H., & Zhu, H. (2026) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4071396 · Cited by 5 · see citing works

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

Cho, A. D., Carrasco, R. A., & Ruz, G. A. (2022) · Sensors · doi:10.3390/s22145323 · Cited by 5 · see citing works

ConTriFormer: triggers-guided contextual informer for remaining useful life prediction of rolling bearings

Pang, B., Hua, Z., Zhao, D., & Xu, Z. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ace46d · Cited by 4 · see citing works

Remaining useful life prediction of rolling bearings based on multi-wavelet-time convolution transformer

Lu, Y., Cheng, S., Zhu, D., Zhao, D., & Gao, Q. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adc9d3 · Cited by 4 · see citing works

YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion

Peng, Y., Zhang, J., Liu, S., Zhang, M., & Guo, Y. (2025) · Sensors · doi:10.3390/s25154862 · Cited by 4 · see citing works

An interpretable variational attention-weighted feature decoupling network for remaining useful life prediction of aero-engines under time-varying operating conditions

Tian, R., Mei, B., Fei, S., Fu, Y., Yang, Y., & Zhu, W. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae2646 · Cited by 3 · see citing works

A novel unsupervised adaptive density-based clustering filter for remaining useful life prediction of bearings

Wang, W., Zhou, G., He, Z., Zhou, P., Ma, G., Yan, X., & Ma, T. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad76c5 · Cited by 3 · see citing works

Dual phase hierarchical brain tumour detection and segmentation using unet based skip guidance residual convolutional transformer

Salve, A. K., & Jondhale, K. C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106927 · Cited by 3 · see citing works

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

Yin, Z., Tan, J., & Wan, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad4b54 · Cited by 3 · see citing works

Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

Li, Q., Zheng, C., Liu, S., Dai, S., Zhang, B., Tang, Y., & Wang, Y. (2024) · IET Renewable Power Generation · doi:10.1049/rpg2.13092 · Cited by 3 · see citing works

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

He, M., Li, Z., & Hu, F. (2024) · Machines · doi:10.3390/machines12110766 · Cited by 3 · see citing works

LT-Mamba: A Novel Network for RUL Estimation of Multisensor Signals

Xie, Y., Lin, X., Liu, F., Qi, H., & Zhao, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3608799 · Cited by 2 · see citing works

A Multiscale Method for Predicting the Remaining Useful Life of Bearings Using Physics-Informed Hypergraph Wavelet Attention Network

Jin, Z., Gao, Y., He, D., Zhao, J., Wu, Z., & Huang, C. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3733432 · Cited by 2 · see citing works

Reaming Useful Life Prediction of Bearings Based on CNN and Encoder Layers

Jin, Y., Lin, T., Zhang, X., Wang, H., Song, L., & Bin, G. (2022) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd57530.2022.10058064 · Cited by 2 · see citing works

Utilizing Hybrid Deep Reinforcement Learning and Transformer Networks for Predictive Maintenance in Product Safety Engineering

Nimma, D., Kannaiyan, M., Gupta, D., Roy, M., Ansari, M. S. A., & Balakumar, A. (2024) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa64000.2024.10882464 · Cited by 2 · see citing works

Bearing Lifespan Reliability Prediction Method Based on Multiscale Feature Extraction and Dual Attention Mechanism

Luo, X., & Wang, M. (2025) · Applied Sciences · doi:10.3390/app15073662 · Cited by 2 · see citing works

High-Precision Repeatability Accuracy Prognostics of CNC Rotary Tables With Multichannel Multivariant Vibration Embeddings and Improved Transformer

Ding, P., Zhang, F., Shen, X., Cui, J., Zhang, J., & Zhao, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3584751 · Cited by 2 · see citing works

A generalization enhanced spatial-time-global competitive network for remaining useful life prediction

Song, L., Zhao, S., Zheng, C., Liu, J., Lin, T., & Wang, H. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/add48c · Cited by 1 · see citing works

Multitask Pointwise Mutual Information Learning for Bearing Remaining Useful Life Cross-Domain Imbalanced Regression

Hu, T., Mo, Z., & Zhang, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3569977 · Cited by 1 · see citing works

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network

Qi, L., Pan, J., Huang, T., Zhou, Z., & Huang, F. (2025) · Applied Sciences · doi:10.3390/app15126401 · Cited by 1 · see citing works

Dental radyografi görüntülerinin sınıflandırılmasına yönelik hibrit ConvViT modeli

Utku, A. (2025) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.1551005 · Cited by 1 · see citing works

Improving robustness in X-ray image classification through attention mechanisms in convolutional neural networks

Alammar, Z., Alzubaidi, L., Zhang, J., Li, Y., Zhao, Z., & Gu, Y. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3447 · Cited by 1 · see citing works

Bidirectional-Graph Attention Networks Parallel Encoder for Data Imputation and Fault Diagnosis of Industrial Robots

Wang, Z., Xiao, W., Chen, C., Wang, T., Yu, Z., Chen, Z., & Xiaoyu Song, B. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3665361 · Cited by 1 · see citing works

An Interpretable State-Aware Variational Network with Dual Invertible Flows for Remaining Useful Life Prediction of Aero-Engines

Tian, R., Mei, B., & Zhu, W. D. (2025) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-xian66756.2025.11427726 · Cited by 1 · see citing works

A Probabilistic Bayesian Transformer for Bearings Remaining Useful Life Prediction

Peng, H., Jiang, B., & Mao, Z. (2023) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess58597.2023.10295921 · Cited by 1 · see citing works

Sim-ConvFormer: A lightweight troubleshooting framework using SimAM and External Attention

Guo, Y., Gao, J., & Gao, G. (2024) · International Conference on Internet of Things and Cloud Computing Technology (IoTCCT) · doi:10.1145/3702879.3702934 · Cited by 1 · see citing works

Domain Adaptive Network Based on Transformer for Remaining Useful Life Prediction in Turbofan EnginePreprint

Zeng, Y., Liu, F., & Han, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5060158

A Convolutional Transformer Architecture for Remaining Useful Life Estimation

Ding, Y., & Jia, M. (2021) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-nanjing52125.2021.9612814 · Cited by 21 · see citing works

Spatial attention-based convolutional transformer for bearing remaining useful life prediction

Chen, C., Wang, T., Liu, Y., Cheng, L., & Qin, J. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac7c5b · Cited by 45 · see citing works

Remaining useful life prediction of rolling bearings based on multi-wavelet-time convolution transformer

Lu, Y., Cheng, S., Zhu, D., Zhao, D., & Gao, Q. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adc9d3 · Cited by 4 · see citing works

Local Enhancing Transformer With Temporal Convolutional Attention Mechanism for Bearings Remaining Useful Life Prediction

Peng, H., Jiang, B., Mao, Z., & Liu, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3291787 · Cited by 44 · see citing works

Bearing Remaining Useful Life Prediction Based on Multi-scale Feature Extraction and Convolutional Attention Mechanism

Yu, Y., Xi, Q., & Wang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9921-6_13