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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68261
Volume:
10 |
10 |
Oct 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 Electronics and Telecommunication
Priyadarshini College of Engineering Nagpur

Department of Electronics and Telecommunication
Priyadarshini College of Engineering Nagpur

Department of Electronics and Telecommunication
Priyadarshini College of Engineering Nagpur

Department of Electronics and Telecommunication
Priyadarshini College of Engineering Nagpur
The IoT-Based Machine Vibration Monitoring and Predictive Maintenance System is developed to continuously monitor the health and operating condition of industrial machines in real time. The system uses a vibration sensor along with temperature and current sensors to collect important machine parameters during operation. An ESP32 development board receives and processes the sensor data to identify abnormal conditions such as excessive vibration, imbalance, misalignment, bearing wear, overheating, and overloading. The monitored data can be displayed on an I2C LCD and transmitted through IoT connectivity to a software or cloud-based dashboard for remote monitoring, visualization, and alert generation. Historical data can also be analyzed to observe machine health trends and plan maintenance before major failures occur. The proposed system helps reduce unexpected breakdowns, machine downtime, maintenance costs, and production losses while improving the reliability and efficiency of industrial machinery.
KEYWORDS
The proposed system demonstrates that IoT and sensor-based monitoring can be effectively used for predictive maintenance of industrial machinery. Continuous monitoring of vibration along with temperature and current helps detect developing faults at an early stage and reduces dependence on manual inspection. Real-time alerts and historical data analysis allow maintenance personnel to take timely action before serious machine failure occurs. Overall, the system can help reduce unexpected breakdowns, machine downtime, maintenance costs, and production losses, while improving the reliability and operational efficiency of industrial machines.
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Publication Date
10/24/2026
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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