PSI - Issue 77

Carolina Francisco et al. / Procedia Structural Integrity 77 (2026) 567–574 C. Francisco et al. / Structural Integrity Procedia 00 (2026) 000–000

574

8

crankshaft bearings, and motor data acquisition to assess overall press condition. Few studies in the literature provide experimental data or detailed processing methods, making it di ffi cult to define an e ff ective monitoring strategy. Analysis of acceleration data from an operating press showed that each component’s response reflects its function and loading conditions, confirming that the measurements are closely linked to both process dynamics and component health. Further data processing is needed to extract deeper insights and support continuous failure prediction. This paper presents the initial stage of developing a condition monitoring system. A platform is being built to process data, estimate press condition, and visualize results, using accelerometer data as a test case. Future work will integrate a multi-body model of the press to form a digital twin, enabling the inclusion of temperature and load data for assessing journal bearings, the tool, and advanced processing strategies.

Acknowledgements

This work has been supported by the European Union under the Next Generation EU, through a grant of the Portuguese Republic’s Recovery and Resilience Plan (PRR) Partnership Agreement, within the scope of the PRODUTECH R3- “Agenda Mobilizadora da Fileira das Tecnologias de Produc¸a˜o para a Reindustrializac¸a˜o”, nr C645808870 - 00000067, investment project no 60.

References

He, X., Welo, T., & Ma, J. (2025). In-process monitoring strategies and methods in metal forming: A selective review. Journal of Manufacturing Processes, 138, 100–128. Huang, C.-Y., & Dzulfikri, Z. (2021). Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network. Sensors, 21(1), 262. C. Zhang, J. Liang, D. Yu, C. Zhang, H. Xiao and M. Wang, ”Application MEMS multi-sensors for monitoring the forming load of stamping press,” 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China, 2012, pp. 1518-1523 Peinado-Asensi, I., Montes, N., & Garc´ıa, E. (2021). Towards real time predictive system for mechanical stamping presses to assure correct slide parallelism. Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics. Presented at the 18th International Conference on Informatics in Control, Automation and Robotics, Online Streaming Mei, S., Yuan, M., Cui, J., Dong, S., & Zhao, J. (2022). Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach. Computers & Industrial Engineering, 168(108129), 108129. Behrens, BA., Krimm, R., Kammler, M. et al. Fatigue analysis of a mechanical press by means of the hybrid multi-body simulation. Prod. Eng. Res. Devel. 6, 421–430 (2012). Kubik, C., Hohmann, J. & Groche, P. Exploitation of force displacement curves in blanking—feature engineering beyond defect detection. Int J Adv Manuf Technol 113, 261–278 (2021). Niemietz, P., Kornely, M.J.K., Trauth, D. et al. Relating wear stages in sheet metal forming based on short- and long-term force signal variations. J Intell Manuf 33, 2143–2155 (2022). Hohmann, J., Schatz, T., Groche, P. (2017). Intelligent Wear Identification Based on Sensory Inline Information for a Stamping Process. In: Majstorovic, V., Jakovljevic, Z. (eds) Proceedings of 5th International Conference on Advanced Manufacturing Engineering and Technologies. NEWTECH 2017. Lecture Notes in Mechanical Engineering. Springer, Cham. Laubichler, C., Kiesling, C., Marques da Silva, M., Wimmer, A., & Hager, G. (2022). Data-Driven Sliding Bearing Temperature Model for Condi tion Monitoring in Internal Combustion Engines. Lubricants, 10(5), S. M., Muzakkir & P., Lijesh & Hirani, Harish. (2015). Failure mode and e ff ect analysis of journal bearing. International Journal of Applied Engineering Research. 10. 37752-37759. Stamboliska, Z., Rusin´ski, E., & Moczko, P. (2015). Introduction—review of today’s industry and role of condition monitoring. In Proactive Condition Monitoring of Low-Speed Machines (pp. 1–8). Gomes, G. F., Mendez, Y. A. D., da Silva Lopes Alexandrino, P., da Cunha, S. S., Jr, & Ancelotti, A. C., Jr. (2019). A review of vibration based inverse methods for damage detection and identification in mechanical structures using optimization algorithms and ANN. Archives of Computational Methods in Engineering. State of the Art Reviews, 26(4), 883–897. J. Jung et al., ”Monitoring of journal bearing faults based on motor current signature analysis for induction motors,” 2015 IEEE Energy Conversion Congress and Exposition (ECCE), Montreal, QC, Canada, 2015, pp. 300-307. Seo, M.-K., & Yun, W.-Y. (2024). Gearbox Condition Monitoring and Diagnosis of Unlabeled Vibration Signals Using a Supervised Learning Classifier. Machines, 12(2), 127. Jancarczyk, D., Wro´bel, I., Danielczyk, P., & Sidzina, M. (2024). Enhancing Vibration Analysis in Hydraulic Presses: A Case Study Evaluation. Applied Sciences, 14(7), 3097.