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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67836
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.
Electronics and telecommunication engineering
Dr. Daulatrao aher collage of engineering karad
Electronics and telecommunication engineering
Dr. Daulatrao aher collage of engineering karad
Electronics and telecommunication engineering
Dr. Daulatrao aher collage of engineering karad
Electronics and telecommunication engineering
Dr. Daulatrao aher collage of engineering karad
Industrial machines are used in manufacturing plants, workshops, processing units and many other working environments. During operation a machine may develop a problem because of excess vibration, a rise in temperature an unusual change in electrical current or an abnormal voltage condition. If these changes are not noticed in time they may lead to machine stoppage, repair costs, production delays and possible safety concerns. This paper presents a proposed Industrial Fault Detection and Monitoring System that uses an ESP32 microcontroller and a group of sensors to observe selected operating conditions. An MPU6050 sensor is proposed for measuring acceleration related to vibration an ACS712 module for measurement a DS18B20 sensor for temperature measurement and a ZMPT101B module for voltage sensing. The ESP32 reads the sensor values checks them against limits selected for the test equipment and can send readings or warning messages through wireless network connectivity to a monitoring dashboard or connected device. The paper introduces the project reviews related research on maintenance describes the block diagram and explains the proposed system in simple terms. The design is intended as a prototype and a starting point for condition monitoring. Its usefulness must be established through construction, sensor calibration, controlled testing and recording of actual results. Since experimental measurements are not available in this draft no detection accuracy or performance improvement is claimed.
KEYWORDS
This paper explained a system that uses the Internet of Things for finding problems and checking machines in industry. It uses an ESP32 microcontroller and sensors for vibration, electrical current, temperature and voltage. The study of what others have done shows why connected checking and predicting when machines need work is important in factories. The system takes sensor readings checks them against set limits and can send the readings and warnings to a checking screen. The system is meant to be a low-cost tool for learning and not a certified safety device for industry. The next step is to make the tool check the sensor connections make sure the numbers are correct choose the limits for the test setup and write down what happens in a safe place. Real tests are needed before saying anything about how good the system's how reliable it is or if it can find certain problems. Future work might improve how data is saved how warnings are. How patterns over time are looked at.
[1] T. Zonta, C. A. Da Costa R. Da Rosa Righi, M. J. De Lima, E. S. Da Trindade and G. P. Li, "Predictive maintenance in the Industry 4.0: A literature review," Computers & Industrial Engineering vol. 150 Art. No. 106889 Dec. 2020 Doi: 10.1016/j.cie.2020.106889. [2] J. Dalzochio, R. Kunst, E. Pignaton, A. Binotto, S. Sanyal J. F. Barbosa and J. A. P. De Freitas "Machine learning and reasoning for maintenance in Industry 4.0: Current status and challenges " Computers in Industry vol. 123 Art. No. 103298 Dec. 2020 Doi: 10.1016/j.compind.2020.103298. [3] D. Guidotti, L. Pandolfo and L. Pulina "A literature review of supervised machine learning techniques for predictive maintenance in Industry 4.0 " IEEE Access, vol. 13 Pp. 102479–102504 2025 Doi: 10.1109/ACCESS.2025.3578686. [4] "Industry 4.0 technologies for manufacturing: A systematic review of machine learning methods for predictive maintenance " in Proc. IEEE International Conference, on Smart Systems and Advanced Applications (ICSSAS) 2023, doi: 10.1109/ICSSAS57918.2023.10331740.
Publication Date
10/12/2026
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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