Abstract
This project developed an integrated system that enabled equipment to autonomously count and monitor the service lifetime of internal machine components, thereby assessing their condition in real time and determining optimal maintenance intervals. By embedding this functionality into the equipment, the system facilitated proactive decision-making for maintenance activities before component failure occurs. Furthermore, a preventive maintenance system was designed, installed, and calibrated to automatically inspect, detect, and issue alerts when any component approached or exceeded its designated service lifespan. This predictive capability significantly reduced the likelihood of unexpected breakdowns, minimizes potential damage, and ultimately shortens machine downtime. The system was implemented and tested in a case study factory specializing in the production of electronic parts. Quantitative data were collected during both the pre-implementation period and after the system was fully operational. The comparison of these datasets allowed for an evaluation of the system's impact on production continuity and maintenance efficiency. This research, adopting a quantitative methodology,
enhanced preventive maintenance practices and improved overall manufacturing performance through more accurate and timely maintenance scheduling.
References
- Chuaitakhu, R. & Jongkol, P. (2025). Improving preventive maintenance of machinery using count lifetime. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM), 35, 38-45.
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Machines and Drives |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | October 20, 2025 |
| Publication Date | October 27, 2025 |
| Submission Date | May 2, 2025 |
| Acceptance Date | June 9, 2025 |
| Published in Issue | Year 2025 Volume: 35 |
