Abstract
The use of digital twins is becoming a foundation for automation that will improve how industries handle and organize data about both virtual and physical things. It enables analyzing industrial data more seamlessly by merging the Internet of Things (IoT) with Artificial Intelligence (AI) to make sense of it. The growth of online retailers has made it harder for logistics professionals to keep people safe, make sure products are of superior quality, and run smoothly. The most recent advancements of digital twin (DT) have made it easier to create predictive maintenance. Using DT makes it easier to accurately assess equipment status and detect problems before they occur, thereby making the system more reliable. This shift from reactive to preventive operations makes maintenance plans more efficient, reduces disruptions, and boosts the company’s profits and competitive advantage. Nevertheless, the research and implementation of Digital Twin (DT) for Predictive Maintenance remains developing, likely due to the incomplete exploration of the function and significance of machine learning (ML) within this context by academics and industry alike. In this paper, a digital twin solution in which the logistics equipment is monitored and continuously maintained through the application of ML algorithms in an IOT environment is proposed. The logistics 2.0-enabled system generates virtual replicas of physical logistics assets, such as forklifts, conveyor belts, automated guided vehicles, cranes, and warehousing robotic, and the digital twins are synchronized in real-time via IoT sensor networks. For anomaly detection, Remaining Unit Life (RUL) prediction, and failure classification, this study utilized Isolation Forest (iForest), Autoencoders, Long Short-Term Memory (LSTM) networks, and Random Forest (RF) machine learning models. The architecture consists of three layers, each of which is connected to the other named the physical layer, including heterogeneous IoT sensors (vibration, temperature, acoustic, and current/voltage, GPS, and load sensors); the digital twin layer, enabling real-time synchronization and simulation; and the ML layer running predictive maintenance and optimization models. Results from the application of the proposed system show a 30–50% reduction in the equipment downtime, 20–40% diminishment in the maintenance cost, longer lifespan for the equipment, and better operational safety.
Similar content being viewed by others
Data availability
The implementation simulates sensor data and does not rely on any external datasets. All data for the digital twin system are generated dynamically within the SensorNetwork class using predefined baseline values combined with controlled random variations to emulate degradation and measurement noise. To ensure transparency and reproducibility, the relevant implementation files and configuration details have been provided in the Supplementary Materials.
References
Karkaria, V. et al. A digital twin framework utilizing machine learning for robust predictive maintenance: Enhancing tire health monitoring. J. Comput. Inform. Sci. Eng. 25(7), 071003 (2025).
Li, J. & Wang, J. Digital twin-driven management strategies for logistics transportation systems. Sci. Rep. 15(1), 12186 (2025).
Gunckel, P. V., Lobos, G., Rodriguez, F. K., Bustos, R. M. & Godoy, D. Methodology proposal for the development of failure prediction models applied to conveyor belts of mining material using machine learning. Reliabil. Eng. Syst. Safety 256, 110709 (2025).
Sajid, M. et al. Enhancing melanoma diagnostic: Harnessing the synergy of ai and CNNS for groundbreaking advances in early melanoma detection and treatment strategies. Int. J. Imag. Syst. Technol. 35(1), 70016 (2025).
Zou, Y., Wu, J., Meng, X., Wang, X. & Manzardo, A. Digital twin integration for dynamic quality loss control in fruit supply chains. J. Food Eng. 397, 112577 (2025).
Sajid, M. et al. Enhancing intrusion detection: A hybrid machine and deep learning approach. J. Cloud Comput. 13(1), 123 (2024).
Sajid, M., Sanaullah, M., Fuzail, M., Malik, T. S. & Shuhidan, S. M. Comparative analysis of text-based plagiarism detection techniques. PloS one 20(4), 0319551 (2025).
Khan, I., Al Rashid, A. & Koç, M. Integration of machine learning and digital twin in additive manufacturing of polymeric-based materials and products. Prog. Addit. Manuf. 10(12), 10685–10737 (2025).
Malik, K. R. et al. A hybrid steganography framework using DCT and GAN for secure data communication in the big data era. Sci. Rep. 15(1), 19630 (2025).
Abed, A. M. & Gaafar, T. S. Hybridize machine learning methods and optimization techniques to analyze and repair welding defects via digital twin of jidoka simulator. IEEE Access 13, 19266–19294 (2025).
Torba, E., Merdani, A., & Sheme, E. Integrating digital twins and machine learning for enhanced predictive maintenance, real-time analytics, and cost savings: A review. In: 2025 20th Annual System of Systems Engineering Conference (SoSE), pp. 1–5 (2025). IEEE
Ren, X. & Zhao, N. The risk management of customized product supply chains based on digital twin technology. Int. J. Prod. Res. 63(24), 10490–10516 (2025).
Sajid, M. et al. Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders. Sci. Rep. 15(1), 3164 (2025).
Ren, Z., Wan, J. & Deng, P. Machine-learning-driven digital twin for lifecycle management of complex equipment. IEEE Trans. Emerg. Top. Comput. 10(1), 9–22 (2022).
Pulcini, V. & Modoni, G. Machine learning-based digital twin of a conveyor belt for predictive maintenance. Int. J. Adv. Manuf. Technol. 133(11), 6095–6110 (2024).
Goel, V., Arora, A., Rani, R., Goel, N., Thakar, P., & Arora, H. Machine learning model for a digital twin for predictive maintenance and optimization in a fruit supply chain. In: 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), pp. 1–8 (2024). IEEE
Kumar, S. & Sharma, S.C., 2024. Intelligent transportation storage condition assessment system for fruits and vegetables supply chain using internet of things enabled sensor network. Int. J. Syst. Assur. Eng. Manag., pp.1-15.
Wu, W. et al. Internet of everything and digital twin enabled service platform for cold chain logistics. J. Indust. Inform. Integrat. 33, 100443 (2023).
Sajid, M., Aslam, N., Abid, M. K. & Fuzail, M. Rded: Recommendation of diet and exercise for diabetes patients using restricted boltzmann machine. VFAST Trans. Softw. Eng. 10(4), 37–55 (2022).
Ahmad, H., Sajid, M., Mazhar, F. & Fuzail, M. Mapping unseen connections: Graph clustering to expose user interaction patterns. J. Fut. Artif. Intell. Technol. 1(4), 474–496 (2025).
Tao, F. et al. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9), 3563–3576 (2018).
Sun, X. et al. Digital twin for smart manufacturing equipment: Modeling and applications. Int. J. Adv. Manuf. Technol. 137(9), 4929–4946 (2025).
Zhong, D., Xia, Z., Zhu, Y. & Duan, J. Overview of predictive maintenance based on digital twin technology. Heliyon 9(4), e14534 (2023).
Sajid, M. et al. Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm. PloS One 20(1), 0307718 (2025).
Bortolini, R., Rodrigues, R., Alavi, H., Vecchia, L. F. D. & Forcada, N. Digital twins’ applications for building energy efficiency: A review. Energies 15(19), 7002 (2022).
Kamble, S. S. et al. Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technol. Forecast. Soc. Change 176, 121448 (2022).
Lu, M. & Antwi-Afari, M. F. A scientometric analysis and critical review of digital twin applications in project operation and maintenance. Eng. Construct. Arch. Manag. 32(10), 6951–6979 (2025).
Sudhakar, G., Rao Chandra, S. V., Sunitha, M., Pulipati, V. & Santhosh Kumar, R. Hybrid AI-based threat prediction and mitigation framework for securing cloud storage. Eur. Phys. J. Plus 140(10), 1–26 (2025).
Cheng, J., Liu, Y., Li, W. & Li, T. Deep reinforcement learning for cost-optimal condition-based maintenance policy of offshore wind turbine components. Ocean Eng. 283, 115062 (2023).
Cheng, Y., Wang, C., Wu, J., Zhu, H. & Lee, C. K. Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Appl. Soft Comput. 118, 108507 (2022).
Adhikari, A., Karunaratne, T., Sumanarathna, & N. Machine learning techniques for building predictive maintenance: a review. In: 23rd EuroFM Research Symposium, pp. 28–39 (2024). Zenodo
Nwulu, E. O., Elete, T. Y., Erhueh, O. V., Akano, O. A. & Omomo, K. O. Machine learning applications in predictive maintenance: Enhancing efficiency across the oil and gas industry. Int. J. Eng. Res. Updates 5(1), 17–30 (2023).
Zhao, W. et al. Application and research trend of digital twin in measurement technology. Meas. Sci. Technol. 35(11), 112003 (2024).
Peruthambi, V. et al. Big data-driven predictive maintenance for industrial IoT (IIoT) systems. Metall. Mater. Eng. 31(3), 21–30 (2025).
Idrissi, Z. K., Lachgar, M. & Hrimech, H. Blockchain, IoT and AI in logistics and transportation: A systematic review. Transp. Econ. Manag. 2, 275–285 (2024).
Singh, S. A. & Balpande, S. S. Development of IoT-based condition monitoring system for bridges. Sound Vib. 56(3), 209–220 (2022).
Sajid, M. et al. Advanced multilayer security framework: Integrating AES and LSB for enhanced data protection. J. Supercomputing 81(17), 1607 (2025).
Chauhan, G. S., Jadon, R. & Awotunde, J. B. Smart IoT analytics: Leveraging device management platforms and real-time data integration with self-organizing maps for enhanced decision-making. Int. J. Appl. Sci. Eng. Manag 15(2), 1–16 (2021).
Sajid, M., Malik, K. R., Khan, A. H., Fuzail, M. & Li, J. Tvae-3d: Efficient multi-view 3d shape reconstruction with diffusion models and transformer based VAE. Cluster Comput. 28(13), 854 (2025).
Sitaraman, S. R. Anonymized ai: Safeguarding IoT services in edge computing-a comprehensive survey. J. Curr. Sci. 10(4), 1–5 (2022).
Tao, F., Zhang, H. & Zhang, C. Advancements and challenges of digital twins in industry. Nat. Comput. Sci. 4(3), 169–177 (2024).
Khan, A. H., Hussain, M. & Malik, M. K. Cardiac disorder classification by electrocardiogram sensing using deep neural network. Complexity 2021(1), 5512243 (2021).
Bazaz, S. M., Lohtander, M. & Varis, J. The prediction method of tool life on small lot turning process-development of digital twin for production. Proc. Manuf. 51, 288–295 (2020).
Zhao, Z., Zhang, M., Chen, J., Qu, T. & Huang, G. Q. Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation. Comput. Indus. Eng. 171, 108454 (2022).
Ma, X., Chen, F., Wang, Z., Li, K. & Tian, C. Digital twin model for chiller fault diagnosis based on SSAE and transfer learning. Build. Environ. 243, 110718 (2023).
Sajid, M., Khan, A. H., Malik, K. R., Khan, J. A. & Alwadain, A. A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model. PeerJ Comput. Sci. 11, 2944 (2025).
He, B. & Bai, K.-J. Digital twin-based sustainable intelligent manufacturing: A review. Adv. Manuf. 9(1), 1–21 (2021).
Wuttke, A., Hunker, J., Rabe, M., & Diepenbrock, J.-P. Estimating parameters with data farming for condition-based maintenance in a digital twin. In: 2023 Winter Simulation Conference (WSC), pp. 1641–1652 (2023). IEEE
Ahmad, M. U., Ashiq, S., Badshah, G., Khan, A. H. & Hussain, M. Feature extraction of plant leaf using deep learning. Complexity 2022(1), 6976112 (2022).
Wang, L., Liu, H., Chen, Z., Zhang, F. & Guo, L. Combined digital twin and hierarchical deep learning approach for intelligent damage identification in cable dome structure. Eng. Struct. 274, 115172 (2023).
Nagra, A. A. et al. A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization. Sci. Rep. 14(1), 19613 (2024).
Madni, A. M., Madni, C. C. & Lucero, S. D. Leveraging digital twin technology in model-based systems engineering. Systems 7(1), 7 (2019).
Bilal, A. et al. PDCNET: Deep convolutional neural network for classification of periodontal disease using dental radiographs. IEEE Access 12, 150147–150168 (2024).
Fang, X. et al. Industry application of digital twin: From concept to implementation. Int. J. Adv. Manuf. Technol. 121(7), 4289–4312 (2022).
Arslan, M. et al. Deep image synthesis, analysis and indexing using integrated CNN architectures. IEEE Access 13, 834–851 (2024).
Nele, L., Mattera, G., Yap, E. W., Vozza, M. & Vespoli, S. Towards the application of machine learning in digital twin technology: A multi-scale review. Discov. Appl. Sci. 6(10), 502 (2024).
Li, W., Zhou, H., Lu, Z. & Kamarthi, S. Navigating the evolution of digital twins research through keyword co-occurence network analysis. Sensors 24(4), 1202 (2024).
Zeneli, M., & Marinova, G. Navigating the future: Digital twin in maritime industry. In: 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), pp. 1–6 (2024). IEEE
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. Digital twins in operation and maintenance (o&p). In: Digital Twin Technologies in Transportation Infrastructure Management, pp. 179–203. Springer, (2023)
Crespo-Aguado, M., Lozano, R., Hernandez-Gobertti, F., Molner, N. & Gomez-Barquero, D. Flexible hyper-distributed IoT-edge-cloud platform for real-time digital twin applications on 6G-intended testbeds for logistics and industry. Future Internet 16(11), 431 (2024).
Lee, D. & Lee, S. Digital twin for supply chain coordination in modular construction. Appl. Sci. 11(13), 5909 (2021).
Zhang, L., Cai, Z.Q., & Ghee, L.J. Virtual commissioning and machine learning of a reconfigurable assembly system. In: 2020 2nd International Conference on Industrial Artificial Intelligence (IAI), pp. 1–6 (2020). IEEE
Iyer, A. A. & Umadevi, K. Design and analysis of twincardio framework to detect and monitor cardiovascular diseases using digital twin and deep neural network. Sci. Rep. 15(1), 24376 (2025).
Wang, X., Zhang, H., Bilal, A., Long, H. & Liu, X. Wgm-dsaga: Federated learning strategies with byzantine robustness based on weighted geometric median. Electronics 12(5), 1190 (2023).
Hashmi, M. U. et al. Resource-limited skew estimation and correction (RLSEC) for edge devices in delay non-tolerant networks. IEEE Access 12, 159597–159610 (2024).
Liu, T., Li, Z., Long, H. & Bilal, A. Nt-gnn: Network traffic graph for 5g mobile iot android malware detection. Electronics 12(4), 789 (2023).
Zhou, Z. et al. Vehicle lateral dynamics-inspired hybrid model using neural network for parameter identification and error characterization. IEEE Trans. Vehicular Technol. 73(11), 16173–16186. https://doi.org/10.1109/TVT.2024.3416317 (2024).
Hu, D. et al. Machine learning–finite element mesh optimization-based modeling and prediction of excavation-induced shield tunnel ground settlement. Int. J. Comput. Methods 22(4), 2450066. https://doi.org/10.1142/S021987622450066X (2025).
Liu, Y., Huo, M., Li, M., He, L. & Qi, N. Establishing a digital twin diagnostic model based on cross-device transfer learning. IEEE Trans. Instrum. Meas. 74, 1–10. https://doi.org/10.1109/TIM.2025.3562973 (2025).
Wan, A. et al. A novel GA-PSO-SVM model for compound fault diagnosis in gearboxes with limited data. IEEE Sensors J. 25(16), 30431–30443. https://doi.org/10.1109/JSEN.2025.3576761 (2025).
Lu, Y. et al. A dynamic imperfect inspection-based maintenance optimization considering dependent competing failure. Measurement 253, 117470. https://doi.org/10.1016/j.measurement.2025.117470 (2025).
He, D., Lin, Y., Dai, Z. & Yang, S. X. Neurodynamics-based visual servo predictive control for improving smooth movement of logistics omnidirectional robots. IEEE Trans. Indus. Electron. https://doi.org/10.1109/TIE.2025.3585028 (2025).
Cao, B. et al. A self-supervised evaluation approach of insulation condition for vehicle cable terminals using hypergraph neural network with dynamic features. IEEE Trans. Indust. Inform. https://doi.org/10.1109/TII.2025.3598442 (2025).
Zhang, B., Xue, Y., Zhang, Y. & Su, K. A self-calibrating digital twin for space thermionic nuclear reactors under fault conditions. Reliab. Eng. Syst. Safety https://doi.org/10.1016/j.ress.2025.111995 (2025).
Luo, J., Wang, G., Li, G. & Pesce, G. Transport infrastructure connectivity and conflict resolution: A machine learning analysis. Neural Comput. Appl. 34(9), 6585–6601. https://doi.org/10.1007/s00521-021-06015-5 (2022).
Acharya, T., Gharekhan, D. & Samal, D. Seismic vulnerability modeling using machine learning and GIS. J. Seismic Explor. 33(3), 1–21 (2024).
Funding
The authors acknowledge the Shaanxi Province Key R&D Plan Project (No. 2019GY-024). Optimization of electric bicycle charging station layout based on adaptive particle swarm algorithm.
Author information
Authors and Affiliations
Contributions
Fang Han conceived and designed the study, supervised the research, and contributed to manuscript writing and revision. Lijun Liu performed the experiments, collected and analyzed the data, and contributed to drafting the manuscript. Junyan Sun assisted with data analysis, methodology development, and manuscript review. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
There are no ethical implications regarding the use of the public dataset.
Informed consent
There are no ethical implications regarding the use of the public dataset.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Han, F., Liu, L. & Sun, J. Logistics equipment condition monitoring and prediction based on digital twin and machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43380-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-43380-4
