Predictive Maintenance in Smart Manufacturing: A Review of Machine Learning and Digital Twin Approaches
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P117Keywords:
Smart Manufacturing, Predictive Analytics, Machine Learning, Digital Twins, Industrial Automation, Maintenance OptimizationAbstract
Predictive maintenance has emerged as a new method to smart manufacturing, allowing firms to forsake reactive and preventative approaches to equipment management based on data analysis. The current study conducts a thorough analysis of digital twin and ML technologies, which jointly aid in the prediction of remaining useful life, defect identification, and anomaly detection in a variety of industrial contexts. Machine learning techniques are scalable, adaptable in nature, and digital twins enable the building of synchronized virtual models that represent the behavior of the physical assets continually delivering real-time information about the deterioration trends, and available systems behavior. Integration of these technologies will facilitate better reliability, less downtime and better use of resources. Nonetheless, literature shows that there are several difficulties such as the inconsistency of sensor data, constraints of the heterogeneous systems interoperability, and issues of model interpretability and industrial implementation. The review notes a necessity of more integrative frameworks that would combine multi-source industrial information with dynamic and continuously learning models. By combining the current advancements, limitations, and implementation gaps, this study will provide a thorough understanding of how predictive maintenance is evolving in smart manufacturing. It also recognizes new opportunities of more autonomous, intelligent, and resilient maintenance ecosystems that can sustain future Industry 4.0 and Industry 5.0 ecosystems.
References
1. S. Mittal, M. A. Khan, D. Romero, and T. Wuest, “Smart manufacturing : Characteristics , technologies and enabling factors,” vol. 233, no. 5, pp. 1342–1361, 2019, doi: 10.1177/0954405417736547.
2. W. Yu, T. Dillon, F. Mostafa, W. Rahayu, and Y. Liu, “A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance,” IEEE Trans. Ind. Informatics, vol. 16, no. 1, pp. 183–192, Jan. 2020, doi: 10.1109/TII.2019.2915846.
3. L. C. Him, Y. Y. Poh, and L. W. Pheng, “IoT-based predictive maintenance for smart manufacturing systems,” in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019, 2019. doi: 10.1109/APSIPAASC47483.2019.9023106.
4. Q. Qiao, J. Wang, L. Ye, and R. X. Gao, “Digital twin for machining tool condition prediction,” Procedia CIRP, vol. 81, no. March, pp. 1388–1393, 2019, doi: 10.1016/j.procir.2019.04.049.
5. V. S. P. Nimmagadda, “Combining Digital Twins and Machine Learning for Real-Time Process Optimization in Manufacturing,” Am. J. Cogn. Comput. AI Syst., vol. 3, pp. 313–349, 2019.
6. B. C. Menezes, J. D. Kelly, A. G. Leal, and G. C. Le Roux, “Predictive, Prescriptive and Detective Analytics for Smart Manufacturing in the Information Age,” IFAC-PapersOnLine, vol. 52, no. 1, pp. 568–573, 2019, doi: 10.1016/j.ifacol.2019.06.123.
7. P. Poor, J. Basl, and D. Zenisek, “Predictive Maintenance 4.0 as next evolution step in industrial maintenance development,” in 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), IEEE, Mar. 2019, pp. 245–253. doi: 10.23919/SCSE.2019.8842659.
8. T. Adimulam, M. Bhoyar, and P. Reddy, “AI-Driven Predictive Maintenance in IoT-Enabled Industrial Systems,” Iconic Res. Eng., vol. 2, no. 11, pp. 398–410, 2019.
9. M. Mabkhot, A. Al-Ahmar, and H. Alkhalefah, “Requirements of the Smart Factory System: A Survey and Perspective,” Machines, vol. 6, no. 2, p. 23, Jun. 2018, doi: 10.3390/machines6020023.
10. P. Zheng et al., “Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives,” Front. Mech. Eng., vol. 13, no. 2, pp. 137–150, 2018.
11. W. Zhang, D. Yang, and H. Wang, “Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey,” IEEE Syst. J., vol. 13, no. 3, pp. 2213–2227, 2019, doi: 10.1109/JSYST.2019.2905565.
12. J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. Wu, “Deep learning for smart manufacturing: Methods and applications,” J. Manuf. Syst., vol. 48, pp. 144–156, Jul. 2018, doi: 10.1016/j.jmsy.2018.01.003.
13. M. Sharp, R. Ak, and T. Hedberg Jr, “A survey of the advancing use and development of machine learning in smart manufacturing,” J. Manuf. Syst., vol. 48, pp. 170–179, 2018.
14. D. F. Hesser and B. Markert, “Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks,” Manuf. Lett., vol. 19, pp. 1–4, Jan. 2019, doi: 10.1016/j.mfglet.2018.11.001.
15. A. Binding, N. Dykeman, and S. Pang, “Machine Learning Predictive Maintenance on Data in the Wild,” in 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019, pp. 507–512. doi: 10.1109/WF-IoT.2019.8767312.
16. A. Abu-Samah, M. K. Shahzad, E. Zamai, and A. B. Said, “Failure Prediction Methodology for Improved Proactive Maintenance using Bayesian Approach,” IFAC-PapersOnLine, vol. 48, no. 21, pp. 844–851, 2015, doi: 10.1016/j.ifacol.2015.09.632.
17. S. Xiang, D. Huang, and X. Li, “A Generalized Predictive Framework for Data Driven Prognostics and Diagnostics using Machine Logs,” in TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, pp. 695–700. doi: 10.1109/TENCON.2018.8650152.
18. M. Grieves, “Digital twin: manufacturing excellence through virtual factory replication,” White Pap., vol. 1, no. 2014, pp. 1–7, 2014.
19. F. Pires, A. Cachada, J. Barbosa, A. P. Moreira, and P. Leitão, “Digital Twin in Industry 4.0: Technologies, Applications and Challenges,” in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, pp. 721–726. doi: 10.1109/INDIN41052.2019.8972134.
20. S. Gupta and A. Mathur, “Modified spray and wait routing in under water acostic communication for sensor network,” in 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE, Dec. 2015, pp. 1–5. doi: 10.1109/ICCIC.2015.7435763.
21. F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” IEEE Trans. Ind. Informatics, vol. 15, no. 4, pp. 2405–2415, 2019, doi: 10.1109/TII.2018.2873186.
22. V. Damjanovic-Behrendt and W. Behrendt, “An open source approach to the design and implementation of Digital Twins for Smart Manufacturing,” Int. J. Comput. Integr. Manuf., vol. 32, no. 4–5, pp. 366–384, 2019.
23. Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, 2018, doi: 10.1109/ACCESS.2018.2793265.
24. P. Aivaliotis, K. Georgoulias, and G. Chryssolouris, “The use of Digital Twin for predictive maintenance in manufacturing,” Int. J. Comput. Integr. Manuf., vol. 32, no. 11, pp. 1067–1080, 2019, doi: 10.1080/0951192X.2019.1686173.
25. T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance,” Comput. Ind. Eng., vol. 137, Nov. 2019, doi: 10.1016/j.cie.2019.106024.
26. Q. Qi, F. Tao, Y. Zuo, and D. Zhao, “Digital Twin Service towards Smart Manufacturing,” Procedia CIRP, vol. 72, pp. 237–242, 2018, doi: 10.1016/j.procir.2018.03.103.
27. W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in manufacturing: A categorical literature review and classification,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1016–1022, 2018, doi: 10.1016/j.ifacol.2018.08.474.
28. F. Tao and M. Zhang, “Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing,” IEEE Access, vol. 5, pp. 20418–20427, 2017, doi: 10.1109/ACCESS.2017.2756069.
29. E. Negri, L. Fumagalli, and M. Macchi, “A Review of the Roles of Digital Twin in CPS-based Production Systems,” Procedia Manuf., vol. 11, pp. 939–948, 2017, doi: 10.1016/j.promfg.2017.07.198.
30. Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
31. Padur, S. K. R. (2020). From centralized control to democratized insights: Migrating enterprise reporting from IBM Cognos to Microsoft Power BI. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 6(1), 218-225.
32. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.
33. Padur, S. K. R. (2019). Machine learning for predictive capacity planning: Evolution from analytical modeling to autonomous infrastructure. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(5), 285-293.
34. Attipalli, A., Enokkaren, S., BITKURI, V., Kendyala, R., KURMA, J., & Mamidala, J. V. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. Available at SSRN 5741305.
35. Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.
36. Padur, S. K. R. (2020). AI augmented disaster recovery simulations: From chaos engineering to autonomous resilience orchestration. International Journal of Scientific Research in Science, Engineering and Technology, 7(6), 367-378.
37. Reddy Padur, S. K. (2021). From Scripts to Platforms-as-Code: The Role of Terraform and Ansible in Declarative Infrastructure Rollouts. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 621-628.
38. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.
39. Padur, S. K. R. (2018). Autonomous cloud economics: AI driven right sizing and cost optimization in hybrid infrastructures. International Journal of Scientific Research in Science and Technology, 4(5), 2090-2097.
40. Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.
41. Padur, S. K. R. (2021). Bridging Human, System, and Cloud Integration through RESTful Automation and Governance. the International Journal of Science, Engineering and Technology, 9(6).
42. Attipalli, A., BITKURI, V., KURMA, J., Enokkaren, S., Kendyala, R., & Mamidala, J. V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. Available at SSRN 5741342.
43. Padur, S. K. R. (2021). From Control to Code: Governance Models for Multi-Cloud ERP Modernization. International Journal of Scientific Research & Engineering Trends, 7(3).
44. Routhu, K. K. (2021). Harnessing AI Dashboards in Oracle Cloud HCM: Advancing Predictive Workforce Intelligence and Managerial Agility. International Journal of Scientific Research & Engineering Trends, 7(6).
45. Padur, S. K. R. (2021). Deep learning and process mining for ERP anomaly detection: Toward predictive and self-monitoring enterprise platforms. Available at SSRN 5605531.
