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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65129
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Vidya Niketan College of Engineering, Bota
Department of Computer Science
Vidya Niketan College of Engineering, Bota
Department of Computer Science
Vidya Niketan College of Engineering, Bota
Department of Computer Science
Vidya Niketan College of Engineering, Bota
Department of Computer Science
Vidya Niketan College of Engineering, Bota
PCs crash when you’re in the middle of a deadline—overheated CPU, full disk, or a fan that just gave up. One blue screen and half your day is gone, plus ₹2,000-₹5,000 to the local repair guy who still can’t promise it won’t happen again next week. Existing tools either log stats or send generic “clean your PC” pop-ups, but nothing tells you exactly when the hard drive will fail or the RAM will choke. Our system runs a tiny Python script on every machine that grabs CPU temp, RAM %, disk speed, battery health, and 15 more metrics every five minutes. It shoots the data to a central Flask server over LAN. There, a per-PC Isolation Forest flags odd spikes and an XGBoost model predicts how many days are left before a part needs attention—accuracy above 85% on lab tests. If the risk score crosses 0.7, the admin dashboard lights up with a clear alert and a one-click “schedule fix” button. No cloud, no subscription, just a browser tab and optional email ping. Everything sits on FastAPI + PostgreSQL, models retrain automatically when new data piles up, and Grafana plots live trends so the IT guy sees the whole lab at a glance. We cut unplanned downtime by 40-60% in dummy runs, turning panic repairs into quiet coffee-break swaps.
KEYWORDS
This work delivers a fully working, dirtcheap predictive maintenance system that any college lab or small office can set up in one afternoon. A tiny client script pushes real PC vitals every five minutes, FastAPI + PostgreSQL on one old i5 box stores everything, per-machine Isolation Forest and XGBoost models predict exactly when the next fan will choke or SSD will die, and the admin gets a clean browser dashboard plus Grafana graphs that turn red days before the crash happens. This project successfully demonstrates an AI-based predictive maintenance system for real-time PC health monitoring. It helps in identifying system failures at an early stage by continuously analyzing hardware performance data. The combination of Isolation Forest and XGBoost improves the accuracy of failure prediction. Automated alerts and live monitoring reduce downtime and improve system reliability. Overall, the system provides an efficient and scalable solution for proactive maintenance of computer systems.
[1] S. S. Alghamdi, A. A. Alshehri, and M. A. Alghamdi, "Machine Learning-Based Predictive Maintenance System for Industrial Yarn Machines," IEEE Int. Conf. Ind. Electron. Appl., Sep. 2024. [2] R. Nadhiya and P. Rajendran, "Machine Learning Techniques for Prediction of Machine Failure Status," Int. J. Recent Res. Pure Appl. Sci., vol. 5, no. 10, Aug. 2025. [3] S. Kumar, R. Singh, and V. Gupta, "Artificial Intelligence for Software Predictive Maintenance Using Log Analysis," IEEE Int. Conf. Comput. Intell. Comput. Res., Dec. 2024. [4] T. Zonta, C. T. de Carvalho, and L. S. da Rosa, "Predictive Maintenance Enabled by Machine Learning: Use Cases and Challenges in the Automotive Industry," Rel. Eng. Syst. Saf., vol. 200, Aug. 2021. [5] M. H. A. Khan, M. A. U. Khan, and A. A.AlBataineh, "Artificial Intelligence for Predictive Maintenance Applications," Appl. Sci., vol. 14, no. 2, Jan. 2024. [6] S. Jahandideh et al., "Machine Learning for Early Patient Decline Detection in Hospitals," Journal of Medical Systems, 2024. [7] psutil Developers, "psutil Cross-platform Process and System Utilities," 2025. [Online]. Available: https://psutil.readthedocs.io [8] FastAPI Documentation, "FastAPI Framework," 2025. [Online]. Available: https://fastapi.tiangolo.com [9] Grafana Labs, "Grafana – The Open Observability Platform," 2025. [Online]. Available: https://grafana.com [10] Scikit-learn Developers, "Isolation Forest and XGBoost Documentation," 2025. [Online]. Available: https://scikit-learn.org
Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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