Cloud-Enabled Industrial Hazard Detection System

Abstract

Nowadays, machine operations and their health status monitoring are the key factors for the production, safety, and cost-effective operations in the modern sector. It is common for traditional condition monitoring systems to be expensive and complicated which made them inaccessible to small and medium-size enterprises. The present work illustrates a system for IoT-based real-time machine monitoring and fault detection that is low-cost and utilizes the ESP8266 NodeMCU microcontroller. The system comprises the sensors MPU6050 (vibration), LM35/DS18B20 (temperature), ACS712 (current), and a sound sensor that pick up the essential machine parameters. The sensor data is processed and sent to the cloud through MQTT or Firebase for visualization and fault analysis. In case of detection of anomalies, a threshold-based detection mechanism activates both local alerts (buzzer, LED) and remote alerts simultaneously. The web dashboard provides visualization of data in real time, detection of faults, and historical analysis of trends. Testing has been done extensively to confirm the stability and quick response of the system under simulated fault conditions. The prototype developed is scalable and open-source, and it can be considered as a step towards the integration of predictive analytics, machine learning, and edge computing, thus facilitating smarter maintenance solutions for Industry 4.0.