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Paper ID:IJSREM68261

IOT based Machine Vibration Monitoring and Predictive Maintenance System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68261

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68261

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.

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Authors

Yukant Pillare

Yukant Pillare

Department of Electronics and Telecommunication

Priyadarshini College of Engineering Nagpur

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Anshul Nagrale

Anshul Nagrale

Department of Electronics and Telecommunication

Priyadarshini College of Engineering Nagpur

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Anish Hatmode

Anish Hatmode

Department of Electronics and Telecommunication

Priyadarshini College of Engineering Nagpur

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Mentors

Prof. A.K.Parmar

Prof. A.K.Parmar

Department of Electronics and Telecommunication

Priyadarshini College of Engineering Nagpur

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Abstract

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

IoT Machine Vibration Monitoring ESP32 Predictive Maintenance Vibration Sensor Condition Monitoring Fault Detection Industrial Automation.

Cite This Article

Yukant Pillare, Anshul Nagrale, Anish Hatmode, Prof. A.K.Parmar, "IOT based Machine Vibration Monitoring and Predictive Maintenance System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026. https://doi.org/10.55041/IJSREM68261

Full Article/Conclusion

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.

References

1.      Altun, D., Akyurt, I., & Demir, S. (2021). Reliability analysis of cement plant machinery: Failure modes andmaintenance strategies. Journal of Engineering for Industry. 2.      Kumar, V., & Raina, A. (2022). Economic impact of downtime in rotary kiln and grinding operations. Cement Industry Review. 3.      Lee, J., Bagheri, B., & Kao, H. A. (2017). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3(1), 18–23. https://doi.org/10.1016/j.mfglet.2015.12.001. 4.      Martins, F., Silva, R., & Pereira, T. (2022). Condition monitoring challenges in heavy-process industries: A case study in cement production. Mechanical Systems and Processing. 5.      Prieto, M., García, R., Fernández, A., & Alonso, J. (2021). Machine learning approaches for predictive maintenance of industrial rotating equipment. Sensors, 21(3), 890–905. https://doi.org/10.3390/s21030890 6.      Ríos, M., Caro, A., & López, D. (2020). IoT applications in industrial predictive maintenance: A review. International Journal of Prognostics. 7.      Sharma, R., & Parey, A. (2020). A review of vibration-based fault diagnosis in rotating machinery. Engineering Failure Analysis. 8.      Singh, A., & Jha, K. (2019). Fault detection and maintenance optimization in cement plant equipment. Cement Engineering International. 9.      Wan, J., Tang, S., & Li, D. (2020). Industrial IoT-based predictive analytics for rotating machinery health monitoring. IEEE Internet of Things Journal, 7(5), 4008–4020.https://doi.org/10.1109/JIOT.2019.2957125  10.   Zhao, S., Wang, Y., & Li, Z. (2021). A deep learning approach for vibration-based machinery fault detection. Mechanical Systems and Signal Processing, 160, 107914. https://doi.org/10.1016/j.ymssp.2021.10791

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Publication Date

10/24/2026

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Review Rounds: 2

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