IoT-based Predictive Maintenance System for Industrial Machines with Machine Learning Algorithms

Sakshi Soni; Aashu Kori

doi:10.55524/ijirem.2026.13.3.11

Abstract

Predictive maintenance is very important in today’s industrial automation due to its significance in minimizing downtime. In this paper, an IOT-based predictive maintenance model for analyzing the state of industrial machines with the use of machine learning algorithms has been proposed. Sensors such as temperature and vibration modules are used to record the live data from the machinery. The collected data is then sent to the cloud through the microcontroller .The machine learning algorithms are used to analyze the data collected and identify any abnormality as well as predict the faults in advance. The performance of the system was tested on an industrial motor together with temperature and vibration sensors. The dataset included normal and faulty conditions for the test. The experiment has shown that the proposed system has above 90% fault detection accuracy. This technique improves the reliability of the equipment as well as cuts down the cost of maintenance. The system also offers the benefit of having real-time monitoring of the machine.

Keywords

Maintenance Forecasting, Internet of Things, Connected Sensor networks, Automated Learning Algorithms, Smart Manufacturing, Thermal Tracking

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Cites this article as

S. Soni, A. Kori, "IoT-based Predictive Maintenance System for Industrial Machines with Machine Learning Algorithms", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-3, Page No-78-84, 2026. Available from: https://doi.org/10.55524/ijirem.2026.13.3.11

Corresponding Author

Sakshi Soni

M. Tech Scholar, Department of Electronics and Communication Engineering, Rameshwaram Institute of Technology & Management, Lucknow, Uttar Pradesh, India

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