International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
IOT Based Preventive Maintenance for Electrical Machine and Industrial Automation Ashwini Arun Ingle1, Prof. Akshay T. Jadhav2, Dr. Ganesh B. Dongre 3 1 M. Tech student, Department of Electronic and Telecommunication, CSMSS Chh. Shahu College of Engineering,
Chh. Sambhaji Nagar (Aurangabad), Maharashtra, India
2 Professor, Department of Electronic and Telecommunication, CSMSS Chh. Shahu College of Engineering,
Chh. Sambhaji Nagar (Aurangabad), Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - IoT-based preventive maintenance has emerged
unexpected breakdowns and makes use of resources more efficiently.
as an advanced solution for improving the reliability and efficiency of electrical machines and industrial automation systems. In this project, smart sensors such as temperature, vibration, and humidity are integrated with microcontrollers and Wi-Fi modules to continuously monitor machine health. The collected data is transmitted to a cloud platform where it is analyzed to identify early signs of wear, overheating, or abnormal operating conditions. Whenever critical thresholds are reached, alerts are generated through buzzers and display units, enabling timely corrective action. This proactive approach minimizes unexpected breakdowns, reduces maintenance costs, extends equipment life, and enhances overall productivity. By combining IoT technology with predictive maintenance strategies, the project demonstrates a practical step toward smarter and more sustainable industrial automation.
Our project is about creating and setting up an IoT system that can be very helpful with preventive maintenance for electrical machines in industries. This project will help to improve the reliability and efficiency of the machines and will also help to reach the goals of Industry 4.0, where smart decisions and data-based maintenance form the basis of modern manufacturing.
2. LITERATURE REVIEW A. Wi-Fi Module A Wi-Fi Module is an electronic component that enables wireless communication between the devices and the internet using standard Wi-Fi protocols. It acts as a bridge that allows microcontrollers, sensors, or embedded systems to transmit and receive data without wired connections. Commonly used modules, such as ESP8266 and ESP32, integrate a microcontroller with built-in TCP/IP stack, making them highly efficient for IoT applications.
Key Words: Temperature, Vibration, Current, Humidity, Sensors, Wi-Fi Module, Cloud Computing, Industrial Automation.
1. INTRODUCTION In today's industrial world, keeping electrical machines and automation systems running without stops is key to making production work better, keeping things safe, and saving money. Old ways of fixing machines, like waiting for problems to happen or fixing them only when they break, often cause unexpected stops, higher costs, and shorter lifespans for equipment. Now, with the Internet of Things (IoT), industries can make changes and can maintain their equipment from just fixing things after they break to predicting and stopping problems before they happen.
Fig 1: Wi-Fi Module In preventive maintenance and industrial automation, Wi-Fi modules provide seamless connectivity for real-time data transfer from machines to cloud servers or monitoring dashboards. Wi-Fi Module supports features like low-power consumption, high data rate, and secure encryption, which are crucial for reliable and scalable in IoT systems. By using a Wi-Fi module, industries can keep an eye on machines from far away, use the data to spot issues before they occur, and make smarter choices, which makes the machines perform better and stay working for longer.
IoT-based maintenance uses smart sensors, cloud computing, and real-time data to keep a close eye on the condition of electrical machines. These sensors track things like temperature, vibration, humidity, and how much energy the machines use. This helps find early signs of wear, problems, or unusual behaviour. By linking these data insights with automation systems, maintenance can be planned ahead of time, which lowers the chance of
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