PREDICTIVE MAINTENANCE FOR DIGITAL PRINTING EQUIPMENT
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6766Keywords:
Digital Printing Equipment, Digital Twin, IoT Sensors, Remaining Useful Life (RUL), Anomaly Detection, Machine Learning, Smart ManufacturingAbstract [English]
This study applies a predictive maintenance model of digital printing equipment based on the concept of IoT-enabled sensing, machine learning analytics, and digital twin simulation that allows predicting faults in real time and optimizing maintenance. A CNNLSTM hybrid model was designed and used to forecast faults and Remaining Useful Life (RUL) by analyzing vibration, temperature, acoustic, and optical data. Multi-sensor printing testbed experimental implementation showed high predictive accuracy (R 2 = 0.94, F1 = 0.93), which decreased the unplanned downtime by 32, maintenance cost by 24, and material waste by 18. The virtual copy of the printing system of digital twin was a dynamic one that enabled continuous synchronization, what-if analysis, and the creation of adaptive alerts. The suggested architecture is environmentally friendly, as it optimizes the energy consumption, increases the lifespan of the components, and reduces the waste which will also meet the Industry 4.0 and smart manufacturing goals. This study defines predictive maintenance as a scalable, cost effective, and environmentally friendly approach of the next generation digital printing ecosystem.
References
Ayvaz, S., and Alpay, K. (2021). Predictive Maintenance System for Production Lines in Manufacturing: A Machine Learning Approach Using IOT Data in Real Time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598 DOI: https://doi.org/10.1016/j.eswa.2021.114598
Borgi, T., Hidri, A., Neef, B., and Naceur, M. S. (2017, January 14–17). Data Analytics for Predictive Maintenance of Industrial Robots. In Proceedings of the 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET) (pp. 412–417). IEEE. https://doi.org/10.1109/ASET.2017.7983729 DOI: https://doi.org/10.1109/ASET.2017.7983729
Calabrese, M., et al. (2020). SOPHIA: An Event-Based IOT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0. Information, 11(4), 202. https://doi.org/10.3390/info11040202 DOI: https://doi.org/10.3390/info11040202
Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., De Beuvron, F. D., Beckmann, A., and Giannetti, C. (2022). KSPMI: A Knowledge-Based System for Predictive Maintenance in Industry 4.0. Robotics and Computer-Integrated Manufacturing, 74, 102281. https://doi.org/10.1016/j.rcim.2021.102281 DOI: https://doi.org/10.1016/j.rcim.2021.102281
Cinar, Z. M., Nuhu, A. A., Zeeshan, Q., and Korhan, O. (2020). Digital Twins for Industry 4.0: A Review. In F. Calisir and O. Korhan (Eds.), Industrial Engineering in the Digital Disruption Era (GJCIE 2019), Lecture Notes in Management and Industrial Engineering (pp. 1–14). Springer. https://doi.org/10.1007/978-3-030-42416-9_1 DOI: https://doi.org/10.1007/978-3-030-42416-9_18
Cinar, Z. M., Zeeshan, Q., Solyali, D., and Korhan, O. (2020). Simulation of Factory 4.0: A Review. In F. Calisir and O. Korhan (Eds.), Industrial Engineering in the Digital Disruption Era (GJCIE 2019), Lecture Notes in Management and Industrial Engineering (pp. 15–28). Springer. https://doi.org/10.1007/978-3-030-42416-9_2 DOI: https://doi.org/10.1007/978-3-030-42416-9_19
Deloitte Touche Tohmatsu Limited (DTTL). (2015). Industry 4.0: Challenges and Solutions for the Digital Transformation and Use of Exponential Technologies. Zurich, Switzerland: Finance, Audit, Tax Consulting Corporate.
Diaz, N., Pascual, R., Ruggeri, F., and Droguett, E. L. (2017). Modeling Age Replacement Policy Under Multiple Time Scales and Stochastic Usage Profiles. International Journal of Production Economics, 188, 22–28. https://doi.org/10.1016/j.ijpe.2017.03.005 DOI: https://doi.org/10.1016/j.ijpe.2017.03.009
Hakeem, A. A., Solyali, D., Asmael, M., and Zeeshan, Q. (2020). Smart Manufacturing for Industry 4.0 Using Radio Frequency Identification (RFID) Technology. Jurnal Kejuruteraan, 32, 31–38. Https://Doi.Org/10.17576/Jkukm-2020-32(1)-05 DOI: https://doi.org/10.17576/jkukm-2020-32(1)-05
Han, Z., Wang, Z., Xie, M., He, Y., Li, Y., and Wang, W. (2021). Remaining Useful Life Prediction and Predictive Maintenance Strategies for Multi-State Manufacturing Systems Considering Functional Dependence. Reliability Engineering and System Safety, 210, 107560. https://doi.org/10.1016/j.ress.2021.107560 DOI: https://doi.org/10.1016/j.ress.2021.107560
Nasir, T., Asmael, M., Zeeshan, Q., and Solyali, D. (2020). Applications of Machine Learning to Friction Stir Welding Process Optimization. Jurnal Kejuruteraan, 32, 171–186. https://doi.org/10.17576/jkukm-2020-32(2)-01 DOI: https://doi.org/10.17576/jkukm-2020-32(2)-01
Russell-Gilbert, A., Sommers, A., Thompson, A., Cummins, L., Mittal, S., Rahimi, S., Seale, M., Jaboure, J., Arnold, T., and Church, J. (2024, December 15–18). Aad-LLM: Adaptive Anomaly Detection Using Large Language Models. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData) (pp. 4194–4203). IEEE. https://doi.org/10.1109/BigData62323.2024.10825679 DOI: https://doi.org/10.1109/BigData62323.2024.10825679
Valis, and Pietrucha-Urbanik, K. (2014). Utilization of Diffusion Processes and Fuzzy Logic for Vulnerability Assessment. Eksploatacja I Niezawodność – Maintenance and Reliability, 16, 48–55.
Yang, H., LaBella, A., and Desell, T. (2022). Predictive Maintenance for General Aviation Using Convolutional Transformers. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, pp. 12636–12642). AAAI. https://doi.org/10.1609/aaai.v36i11.21505 DOI: https://doi.org/10.1609/aaai.v36i11.21538
Zhang, N., Vergara-Marcillo, C., Diamantopoulos, G., Shen, J., Tziritas, N., Bahsoon, R., and Theodoropoulos, G. (2024). Large Language Models for Explainable Decisions in Dynamic Digital Twins. Arxiv Preprint Arxiv:2405.14411. https://doi.org/10.1007/978-3-031-94895-4_8 DOI: https://doi.org/10.1007/978-3-031-94895-4_8
Zhang, W., Yang, D., and Wang, H. (2019). Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Systems Journal, 13(3), 2213–2227. https://doi.org/10.1109/JSYST.2019.2905565 DOI: https://doi.org/10.1109/JSYST.2019.2905565
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Copyright (c) 2025 Mr. Mithun Kumar S, Prakriti Kapoor, Ankesh gupta, Piyush Pal, Vijayakumar K, Bharat Bhushan, Sathyabalaji Kannan
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