Key Technologies for Digital Transformation and Performance Optimization of Manufacturing Enterprises Based on Deep Learning
Main Article Content
Abstract
With the rapid development of intelligent sensing, industrial wireless communication, and Electromagnetic Waves, Antennas and Propagation technologies, the efficient integration of heterogeneous manufacturing data has become essential for digital transformation and operational optimization. This study proposes a deep learning-driven technology framework for performance optimization in manufacturing enterprises by integrating multimodal data fusion, attention-enhanced long short-term memory (LSTM) networks, deep reinforcement learning, and edge deployment. Multi-source equipment signals, including vibration, current, and temperature data, are aligned and fused to construct representative health features for remaining useful life prediction and fault diagnosis. The predicted health states are further utilized to dynamically optimize maintenance scheduling through reinforcement learning, forming a closed-loop architecture from state perception to intelligent decision-making. Experimental validation demonstrates that the proposed framework achieves 96.8% fault prediction accuracy, improves Overall Equipment Effectiveness by 11.8%, and significantly reduces unplanned downtime while enabling low-latency edge inference for real-time industrial applications. The proposed methodology provides an effective solution for data-driven digital transformation and offers valuable engineering references for distributed sensing, industrial wireless monitoring, and intelligent communication systems associated with Electromagnetic Waves, Antennas and Propagation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
J. Matschewsky, L. Kambanou M, and T. Sakao, “From mass customization to circular customization-measuring and managing variety in product service systems,” Resources, Conservation and Recycling, vol. 219, no. 1, pp. 108261-108268, 2025, doi: 10.1016/j.resconrec.2025.108261.
R. Lorenz, M. Kraus, H. Wolf, S. Feuerriegel, and H. Netland T, “Selecting advanced analytics in manufacturing: a decision support model,” Production Planning & Control, vol. 35, no. 7, pp. 711-724, 2024, doi: 10.1080/09537287.2022.2126951.
A. Shokrani, H. Dogan, D. Burian, D. Nwabueze T, P. Kolar, Z. Liao, A. Sadek, et al., “Sensors for in-process and on-machine monitoring of machining operations,” CIRP Journal of Manufacturing Science and Technology, vol. 51, no. 1, pp. 263-292, 2024, doi: 10.1016/j.cirpj.2024.05.001.
U. Hassan I, K. Panduru, and J. Walsh, “Review of data processing methods used in predictive maintenance for next generation heavy machinery,” Data, vol. 9, no. 5, pp. 69-77, 2024, doi: 10.3390/data9050069.
B. Yu, Z. Yang, Y. Yu, and G. Xiang, “GMVE: Graph-Mamba Variational Encoder for Interpretable Remaining Useful Life Prediction with Uncertainty Quantification,” Knowledge-Based Systems, vol. 328, no. 25, pp. 114217-114226, 2025, doi: 10.1016/j.knosys.2025.114217.
A. Andres, A. Martinez-Seras, I. Laña, and D. Ser J, “On the black-box explainability of object detection models for safe and trustworthy industrial applications,” Results in Engineering, vol. 24, no. 1, pp. 103498-103505, 2024, doi: 10.1016/j.rineng.2024.103498.
Y. Wei, Y. Wang, S. Lu, X. Li, and H. Su, “Adaptive integration of closed-loop manufacturing scheduling and predictive maintenance based on RUL prediction by CNN-SVR model,” Expert Systems with Applications, vol. 311, no. 15, pp. 131269-131276, 2026, doi: 10.1016/j.eswa.2026.131269.
F. Wei, X. Ma, Q. Qiu, Y. Ma, J. Wang, and L. Yang, “Adaptive mission risk control under incomplete health information and resource limitation: A constrained multi-state predictive maintenance model,” Reliability Engineering & System Safety, vol. 266, no. 1, pp. 111697-111704, 2025, doi: 10.1016/j.ress.2025.111697.
Q. Zhao, X. Zhang, J. Xie, et al., “KFC-Former: An interpretable kernel function convolutional-former with phase space reconstruction for remaining useful life prediction of mechanical equipment,” Expert Systems with Applications, vol. 302, no. 15, pp. 130472-130481, 2025, doi: 10.1016/j.eswa.2025.130472.
P. Huang, Y. Wang, Y. Gu, and G. Qiu, “A bearing RUL prediction approach of vibration fault signal denoise modeling with Gate-CNN and Conv-transformer encoder,” Measurement Science and Technology, vol. 35, no. 6, pp. 066104-066112, 2024, doi: 10.1088/1361-6501/ad2cd9.
Y. Li, N. Li, J. Ren, and W. Shen, “An interpretable light attention-convolution-gate recurrent unit architecture for the highly accurate modeling of actual chemical dynamic processes,” Engineering, vol. 39, no. 1, pp. 104-116, 2024, doi: 10.1016/j.eng.2024.07.009.
B. Zhao, W. Zhang, Y. Zhang, C. Zhang, C. Zhang, and J. Zhang, “Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization,” Applied Energy, vol. 379, no. 1, pp. 124713-124720, 2025, doi: 10.1016/j.apenergy.2024.124713.
J. Zeng and Z. Liang, “Predictive group maintenance using probabilistic prognostics and deep reinforcement learning,” Computers & Industrial Engineering, vol. 212, no. 1, pp. 111738-111745, 2025, doi: 10.1016/j.cie.2025.111738.
Y. Liu, J. Gao, T. Jiang, and Z. Zeng, “Selective maintenance and inspection optimization for partially observable systems: An interactively sequential decision framework,” IISE Transactions, vol. 55, no. 5, pp. 463-479, 2023, doi: 10.1080/24725854.2022.2062627.
T. Wang, J. Guo, B. Zhang, G. Yang, and D. Li, “Deploying AI on edge: Advancement and challenges in edge intelligence,” Mathematics, vol. 13, no. 11, pp. 1878-1886, 2025, doi: 10.3390/math13111878.
M. Wang, H. Liu, H. Tang, et al., “IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises,” Information Fusion, vol. 115, no. 1, pp. 102750-102759, 2025, doi: 10.1016/j.inffus.2024.102750.
X. Yan, W. Liang, and S. Sun, “An improved method for predicting the remaining useful life using a spatial-temporal feature extraction network with attention mechanism,” IEEE Access, vol. 12, no. 1, pp. 66587-66604, 2024, doi: 10.1109/ACCESS.2024.3396156.
Z. Zhou, D. Yu, M. Chen, Y. Qiao, Y. Hu, and W. He, “An edge intelligence-based model deployment method for CNC systems,” Journal of Manufacturing Systems, vol. 74, no. 1, pp. 716-751, 2024, doi: 10.1016/j.jmsy.2024.04.029.
A. Sama, S. Warnars H L H, H. Prabowo, Meyliana, and N. Hidayanto A, “Acquiring Automation and Control Data in The Manufacturing Industry: A Systematic Review,” Procedia Computer Science, vol. 227, no. 1, pp. 214-222, 2023, doi: 10.1016/j.procs.2023.10.519.
J. Ni, S. Yang, and Y. Liu, “Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN,” Applied Sciences, vol. 15, no. 4, pp. 1801-1809, 2025, doi: 10.3390/app15041801.
A. Ghosh, “Advanced time-series data management for continuous process optimization in manufacturing execution system,” International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 1, pp. 2681-2688, 2022, doi: 10.56726/IRJMETS29797.
M. Barros, P. Fontenele L, S. Silva M, T. Rissi, E. Cabrera, A. Tannuri E, S. Gomi E, et al., “Embracing data irregularities in multivariate time series,” Applied Soft Computing, vol. 186, no. 1, pp. 114039-114045, 2025, doi: 10.1016/j.asoc.2025.114039.
C. Kihal M, H. Moulai, H. Azizi, C. Smartt, and P. Thomas D W, “Sequential adaptive sampling based on cubic spline interpolation to accelerate the near field scanning,” Journal of Electromagnetic Waves and Applications, vol. 36, no. 16, pp. 2237-2259, 2022, doi: 10.1080/09205071.2022.2071767.
Y. Lv, J. Wang, C. Zhang, and J. Ding, “Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction,” Measurement and Control, vol. 58, no. 2, pp. 185-195, 2025, doi: 10.1177/00202940241253173.
S. Guha and A. Datta, “GDRNet: A Deep Learning Framework for Dynamic Sliding Window Determination and Improved Prediction Accuracy in Online IIoT Systems,” IEEE Access, vol. 13, no. 1, pp. 200382-200393, 2025, doi: 10.1109/ACCESS.2025.3634671.
H. Bao, L. Song, Z. Zhang, B. Han, J. Wang, J. Ma, and X. Jiang, “Prediction of the remaining useful life of rolling bearings by LSTM based on multidomain characteristics and a dual-attention mechanism,” Journal of Mechanical Science and Technology, vol. 37, no. 9, pp. 4583-4596, 2023, doi: 10.1007/s12206-023-0814-x.
S. Rathore M and P. Harsha S, “Prognostics analysis of rolling bearing based on bi-directional LSTM and attention mechanism,” Journal of Failure Analysis and Prevention, vol. 22, no. 2, pp. 704-723, 2022, doi: 10.1007/s11668-022-01357-1.
C. Coelho, P. Costa M F, and L. Ferrás L, “Enhancing continuous time series modelling with a latent ODE-LSTM approach,” Applied Mathematics and Computation, vol. 475, no. 1, pp. 128727-128735, 2024, doi: 10.1016/j.amc.2024.128727.
C. He, L. Chen, N. Sun, P. Chen, X. Xu, and S. Lu, “Redundancy modification and potential feature reactivation network for predicting the remaining useful life of machines,” IEEE Sensors Journal, vol. 25, no. 3, pp. 5060-5072, 2024, doi: 10.1109/JSEN.2024.3514168.
R. Dutta Baruah and M. Munoz-Organero, “Recurrent neural network with contextual attention for prognostics,” Evolving Systems, vol. 16, no. 4, pp. 117-125, 2025, doi: 10.1007/s12530-025-09744-3.
H. Zhang, X. Wei, Z. Liu, Y. Ding, and Q. Guan, “Condition-based maintenance for multi-state systems with prognostic and deep reinforcement learning,” Reliability Engineering & System Safety, vol. 255, no. 1, pp. 110659-110667, 2025, doi: 10.1016/j.ress.2024.110659.
S. Eidi, F. Haghighi, A. Safari, and E. Zio, “Optimal dynamic state-dependent maintenance policy by deep reinforcement learning,” Quality and Reliability Engineering International, vol. 41, no. 6, pp. 2715-2728, 2025, doi: 10.1002/qre.3806.
Y. Zhang, B. Cai, C. Gao, Y. Zhao, X. Shao, and C. Yang, “A system-centred predictive maintenance re-optimization method based on multi-agent deep reinforcement learning,” Expert Systems with Applications, vol. 274, no. 1, pp. 127034-127041, 2025, doi: 10.1016/j.eswa.2025.127034.
X. Ji, F. Gong, N. Wang, J. Xu, and X. Yan, “Cloud-edge collaborative service architecture with large-tiny models based on deep reinforcement learning,” IEEE Transactions on Cloud Computing, vol. 13, no. 1, pp. 288-302, 2025, doi: 10.1109/TCC.2024.3525076.
M. Ladegourdie and J. Kua, “Performance analysis of opc ua for industrial interoperability towards industry 4.0,” IoT, vol. 3, no. 4, pp. 507-525, 2022, doi: 10.3390/iot3040027.
I. Liu H, M. Galindo, H. Xie, K. Wong L, H. Shuai H, H. Li Y, and H. Cheng W, “Lightweight deep learning for resource-constrained environments: A survey,” ACM Computing Surveys, vol. 56, no. 10, pp. 1-42, 2024, doi: 10.1145/3657282.