IOT-Based Machine Health Monitoring System

Abstract: Motors, pumps, compressors, generators and conveyors underpin modern manufacturing, but each can break down without warning, leading to unplanned stoppages, expensive repairs and growing maintenance costs. This paper describes an Internet of Things (IOT) system that continuously supervises such equipment. An ESP32 microcontroller reads temperature and vibration sensors and transmits the readings over Wi-Fi to a FastAPI backend, which stores them in a MySQL database and serves them to a ReactJS dashboard. Each reading is evaluated against limits configured for the individual machine, and the machine is classified as Healthy, Warning or Critical. A Critical state triggers a local buzzer and a remote notification through Blynk Cloud. In addition, a machine learning module examines the stored sensor history to anticipate probable faults and so supports predictive maintenance. The result is a low-cost, intelligent approach to industrial condition monitoring.

Keywords: IOT, Predictive Maintenance, Machine Health Monitoring, Machine Learning, ESP32