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Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer

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Active Disturbance Rejection Adaptive Control of Hydraulic Servo Systems

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Fuzzy Adaptive Finite-Time Control Design for Nontriangular Stochastic Nonlinear Systems

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Adaptive Control of Hydraulic Actuators With LuGre Model-Based Friction Compensation

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Adaptive Backstepping Control of an Electrohydraulic Actuator

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Evaluation of artificial intelligence techniques in disease diagnosis and prediction

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Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective

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An integrated multi-head dual sparse self-attention network for remaining useful life prediction

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An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection

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Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review

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Data-driven machinery fault diagnosis: A comprehensive review

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ITFuse: An interactive transformer for infrared and visible image fusion

Tang, W., He, F., & Liu, Y. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110822 · Cited by 199

Spatial correlation and temporal attention-based LSTM for remaining useful life prediction of turbofan engine

Tian, H., Yang, L., & Ju, B. (2023) · Measurement · doi:10.1016/j.measurement.2023.112816 · Cited by 147

Small data challenges for intelligent prognostics and health management: a review

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LiDAR odometry survey: recent advancements and remaining challenges

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ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps

Li, Y., Wang, H., & Sun, M. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109850 · Cited by 143

Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds

Pang, B., Liu, Q., Sun, Z., Xu, Z., & Hao, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102304 · Cited by 128

DVGTformer: A dual-view graph Transformer to fuse multi-sensor signals for remaining useful life prediction

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Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life

Xiang, F., Zhang, Y., Zhang, S., Wang, Z., Qiu, L., & Choi, J.-H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121859 · Cited by 128

Deep learning-based welding image recognition: A comprehensive review

Liu, T., Zheng, P., & Bao, J. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.05.026 · Cited by 126

Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions

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Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

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Multiplexed fiber-optic photoacoustic sensors for simultaneous detection of multi-point gases

Li, C., Han, X., Ma, F., Zhao, X., Wang, Z., Qi, H., Guo, M., & Chen, K. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134801 · Cited by 103

A semantic-driven coupled network for infrared and visible image fusion

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Advances in artificial intelligence for structural health monitoring: A comprehensive review

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Enhancing lithium-ion battery monitoring: A critical review of diverse sensing approaches

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Intelligent fault diagnosis methods toward gas turbine: A review

Liu, X., Yingjie, C. H. E. N., Xiong, L., Wang, J., Luo, C., Zhang, L., & Kehuan, W. A. N. G. (2023) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2023.09.024 · Cited by 103

Review of machine learning in robotic grasping control in space application

Jahanshahi, H., & Zhu, Z. H. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.04.012 · Cited by 94

A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection

Wang, G.-Q., Zhang, C., Chen, M.-S., Lin, Y. C., Tan, X.-H., Kang, Y., Wang, Q., Zeng, W., & Zhao, W. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102280 · Cited by 91

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

Kumari, N., Sharma, A. P., Tran, B., Chilamkurti, N., & Alahakoon, D. (2023) · Energies · doi:10.3390/en16145525 · Cited by 100

A review on infrared and visible image fusion algorithms based on neural networks

Yang, K., Xiang, W., Chen, Z., Zhang, J., & Liu, Y. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104179 · Cited by 84

A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends

Ni, S., Al-qaness, M. A. A., Hawbani, A., Al-Alimi, D., Abd Elaziz, M., & Ewees, A. A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112235 · Cited by 74

Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment

Yao, W., Shi, H., & Zhao, H. Y. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103622 · Cited by 72

Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns

Xiong, J., Zhou, J., Ma, Y., Zhang, F., & Lin, C. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109244 · Cited by 71

CFD-based hydrodynamic performance investigation of autonomous underwater vehicles: A survey

Hong, L., Wang, X., & De-sheng, Z. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117911 · Cited by 69

Efficient self-attention mechanism and structural distilling model for Alzheimer’s disease diagnosis

Zhu, J., Tan, Y. H. V., Lin, R., Miao, J., Fan, X., Zhu, Y., Liang, P., Gong, J., & He, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105737 · Cited by 74

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 71

CS 2 Fusion: Contrastive learning for Self-Supervised infrared and visible image fusion by estimating feature compensation map

Wang, X., Guan, Z., Qian, W., Cao, J., Liang, S. Y., & Yan, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102039 · Cited by 66

TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis

Chen, X., Yang, R., Xue, Y., Song, B., & Wang, Z. (2024) · Control Engineering Practice · doi:10.1016/j.conengprac.2024.105900 · Cited by 66

Dual disentanglement domain generalization method for rotating Machinery fault diagnosis

Zhang, G., Kong, X., Ma, H., Wang, Q., Du, J., & Wang, J. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112460 · Cited by 64

Systematic review: Ultrasonic technology for detecting rail defects

Xue, Z., Xu, Y., Hu, M., & Li, S. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.130409 · Cited by 63

Generative adversarial networks for prognostic and health management of industrial systems: A review

Li, Q. '., Tang, Y., & Chu, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124341 · Cited by 64

A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis

Li, Q. '. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120517 · Cited by 65

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

Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain

Li, S., Jiang, Q., Xu, Y., Feng, K., Zhao, Z., Sun, B., & Huang, G. Q. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102681 · Cited by 60

The state of the art in key technologies for autonomous underwater vehicles: a review

Ma, D., Li, Y., Ma, T., & Pascoal, A. (2025) · Engineering · doi:10.1016/j.eng.2025.08.002 · Cited by 59

A hierarchical attention detector for bearing surface defect detection

Ma, J., Hu, S., Fu, J., & Chen, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122365 · Cited by 60

RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network

Li, J., Mao, W., Yang, B., Meng, Z., Tong, K., & Yu, S. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109854 · Cited by 58

Fiber-optic trace gas sensing based on graphite excited photoacoustic wave

Han, X., Li, C., Guo, M., Zhao, X., Wang, Z., Qi, H., & Chen, K. (2024) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2024.135546 · Cited by 57

Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

Li, C., & Guo, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.024 · Cited by 56

A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data

Tian, J., Han, D., Karimi, H. R., Zhang, Y., & Shi, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106167 · Cited by 59

A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction

Cheng, Y., Qv, J., Feng, K., & Han, T. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110188 · Cited by 63

Deep learning in industrial machinery: A critical review of bearing fault classification methods

Rehman, A. U., Jiao, W., Jiang, Y., Wei, J., Sohaib, M., Sun, J., Shiju, E., Rehman, K. U., & Chi, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112785 · Cited by 59

Integrated fiber-optic Fabry–Perot vibration/acoustic sensing system based on high-speed phase demodulation

Li, C., Zhao, X., Qi, H., Wang, Z., Xu, Y., Han, X., Huang, J., Guo, M., & Chen, K. (2023) · Optics & Laser Technology · doi:10.1016/j.optlastec.2023.110131 · Cited by 53

Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions

Zhou, H., Lei, Z., Zio, E., Wen, G., Liu, Z., Su, Y., & Chen, X. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110139 · Cited by 51

Identification of rice disease under complex background based on PSOC-DRCNet

Liu, Z., Zhou, G., Zhu, W., Chai, Y., Li, L., Wang, Y., Hu, Y., Dai, W., Liu, R., & Sun, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123643 · Cited by 53

Few-shot learning for structural health diagnosis of civil infrastructure

Xu, Y., Fan, Y., Bao, Y., & Li, H. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102650 · Cited by 53

Very short-term wind power forecasting considering static data: An improved transformer model

Wang, S., Sun, Y., Zhang, W., Chung, C. Y., & Srinivasan, D. (2024) · Energy · doi:10.1016/j.energy.2024.133577 · Cited by 51

Exploring human motions for smart wearables: Energy conversion, harvesting and self–powered sensing

Dong, Y., Duan, L., Mao, X., Gu, T., Gu, Y., Yu, H., Zhang, X., Ye, T., Wang, X., Li, P., et al. (2025) · Nano Energy · doi:10.1016/j.nanoen.2025.111289 · Cited by 51

Progress in self-powered, multi-parameter, micro sensor technologies for power metaverse and smart grids

Fan, Y., Zhang, L., Li, D., & Wang, Z. L. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108959 · Cited by 50

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani, S. B., Cummins, L., Killen, B., Carley, R., Amirlatifi, A., Rahimi, S., Seale, M. A., & Bian, L. (2023) · IEEE Access · doi:10.1109/access.2023.3267960 · Cited by 55

New sparse regularization approach for extracting transient impulses from fault vibration signal of rotating machinery

Li, Q. '. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111101 · Cited by 49

Lab on a single fiber: A three-parameter sensor based on triple-SPR for one-to-one detection of DNA, pH and temperature

Gong, P., Li, X., Zhao, Q., Zhou, X., Zhang, Y., & Zhao, Y. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134957 · Cited by 48

Swin MAE: Masked autoencoders for small datasets

Xu, Z., Dai, Y., Liu, F., Chen, W., Liu, Y., Shi, L., Liu, S., & Zhou, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107037 · Cited by 47

Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis

Xu, J., Yuan, C., Ma, X., Shang, H., Shi, X., & Zhu, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110450 · Cited by 46

Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions

Zemouri, R. M. (2025) · Machines · doi:10.3390/machines13020125 · Cited by 57

A Lightweight Military Target Detection Algorithm Based on Improved YOLOv5

Du, X., Song, L., Lv, Y., & Qiu, S. (2022) · Electronics · doi:10.3390/electronics11203263 · Cited by 46

Cross-domain fault diagnosis method for rolling bearings based on contrastive universal domain adaptation

Kang, S., Tang, X., Wang, Y., Wang, Q., & Xie, J. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.12.019 · Cited by 45

Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention

Zhu, T., Chen, Z., Zhou, D., Xia, T., & Pan, E. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110161 · Cited by 45

A deep belief rule base-based fault diagnosis method for complex systems

Zhao, B., Zhang, Q., He, W., Han, P., Cao, Y., & Zhou, G. (2024) · ISA Transactions · doi:10.1016/j.isatra.2024.05.019 · Cited by 45

A survey on few-shot learning for remaining useful life prediction

Mo, R., Zhou, H., Yin, H., & Si, X. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110850 · Cited by 46

Multi-source heterogeneous information fusion fault diagnosis method based on deep neural networks under limited datasets

Han, D., Zhang, Y., Yu, Y., Tian, J., & Shi, P. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111371 · Cited by 44

Observer-Based Fuzzy Adaptive Dynamic Surface Force Control for Pneumatic Polishing System End-Effector With Uncertain Contact Environment Model

Yang, Z., Zhao, W., Kou, J., Ma, Y., Sun, Z., Wang, Y., & Shi, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3585136 · Cited by 42

A review on adversarial–based deep transfer learning mechanical fault diagnosis

Guo, Y., Cheng, Z., Zhang, J., Sun, B., & Wang, Y. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-01006-4 · Cited by 41

Knowledge distillation and teacher–student learning in medical imaging: Comprehensive overview, pivotal role, and future directions

Li, X., Li, L., Li, M., Yan, P., Feng, T., Luo, H., Zhao, Y. L., & Shen, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103819 · Cited by 48

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali, M. A., Irfan, M. S., Khan, T., Khalid, M. Y., & Umer, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-31672-y · Cited by 40

Single-domain incremental generation network for machinery intelligent fault diagnosis under unknown working speeds

Pu, Y., Tang, J. X., Li, X., Wei, C., Huang, W., & Ding, X. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102400 · Cited by 41

A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance

Gupta, S., Kumar, A., & Maiti, J. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106590 · Cited by 48

Low-frequency fiber bragg grating accelerometer based on diaphragm-type cantilever

Fan, W., Wen, J., Gao, H., & Qiao, X. (2022) · Optical Fiber Technology · doi:10.1016/j.yofte.2022.102888 · Cited by 40

Multi-focus image fusion via interactive transformer and asymmetric soft sharing

Zhai, H., Zheng, W., Ouyang, Y., Pan, X., & Zhang, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107967 · Cited by 39

Bearing fault diagnosis using Gradual Conditional Domain Adversarial Network

Wu, C., Zhao, D., Han, T., & Xia, Y. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111580 · Cited by 39

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

Li, J., Zhang, X., Yue, K., Chen, J., Chen, Z., & Li, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123836 · Cited by 38

A review of maritime equipment prognostics health management from a classification society perspective

Qin, L., Knutsen, K. E., Vanem, E., Æsøy, V., & Zhang, H. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117619 · Cited by 50

A Siamese Vision Transformer for Bearings Fault Diagnosis

He, Q., Li, S., Bai, Q., Zhang, A., Yang, J., & Shen, M. (2022) · Micromachines · doi:10.3390/mi13101656 · Cited by 39

General feature spatial location and distance-based unknown Detection: A universal domain adaptation fault diagnosis framework of rotating Machinery

Dong, Y., Xu, M., Li, Y., & Wang, R. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110979 · Cited by 38

Disturbance Observer-Based Fixed-Time Event-Triggered Control for Networked Electro-Hydraulic Systems with Input Saturation

Wang, C., Wang, J., Guo, Q., Liu, Z., & Chen, C. L. P. (2025) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3429638 · Cited by 36

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM

Wu, J., Kong, L., Kang, S., Zuo, H., Yang, Y., & Cheng, Z. (2024) · Sensors · doi:10.3390/s24030780 · Cited by 36

In situ calibration of triaxial coils of a vector optically pumped magnetometers based on a particle swarm optimization algorithm

Li, S., Ma, D., Lu, J., Xing, B., Wang, K., Gao, Y., & Han, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.111878 · Cited by 36

A self-supervised contrastive learning framework with the nearest neighbors matching for the fault diagnosis of marine machinery

Wang, R., Chen, H., & Guan, C. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113437 · Cited by 36

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Ding, L., & Li, Q. '. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1eb3 · Cited by 35

A hybrid prognosis method based on health indicator and wiener process: The case of multi-sensor monitored aero-engine

Yang, X., Gao, X., Zheng, H., Yang, M., & Liu, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110099 · Cited by 37

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

Sun, Y., Li, S., Gao, H., ZHANG, X., Lv, J., Weixiong, L. I. U., & Wu, Y. (2023) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2023.03.024 · Cited by 35

Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects

Guo, Y., Zhang, J., Sun, B., & Wang, Y. (2023) · Sensors · doi:10.3390/s23167263 · Cited by 34

Empowering intelligent manufacturing with edge computing: A portable diagnosis and distance localization approach for bearing faults

Fang, H., An, J., Sun, B., Chen, D., Bai, J., Liu, H., Xiang, J., Bai, W., Wang, D., Fan, S., et al. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102246 · Cited by 34

Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction

Yang, J., & Wang, X. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.109928 · Cited by 34

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., Yang, J., & Hou, J. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110121 · Cited by 34

Multi-Task Intelligent Monitoring of Construction Safety Based on Computer Vision

Liu, L., Guo, Z., Liu, Z., Zhang, Y., Cai, R., Hu, X., Ran, Y., & Wang, G. (2024) · Buildings · doi:10.3390/buildings14082429 · Cited by 34

DT-F Transformer: Dual transpose fusion transformer for polarization image fusion

Liu, J., Shutao Li, A., Dian, R., & Ze, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102274 · Cited by 33

Pre-training enhanced unsupervised contrastive domain adaptation for industrial equipment remaining useful life prediction

Li, H., Cao, P., Wang, X., Li, Y., Yi, B., & Huang, M. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102517 · Cited by 37

Causal graph-based spatial–temporal attention network for RUL prediction of complex systems

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