An Embedded Intelligent System For Detecting & Monitoring Industrial Hazards Event

Abstract

Industrial environments are inherently susceptible to life-threatening hazards, including gas leaks, sudden temperature surges, mechanical vibrations, and equipment failures. If left undetected, these events can cause severe accidents, production disruptions, and significant financial losses. Conventional monitoring approaches primarily manual inspections and threshold based alarm systems  are reactive, error-prone, and incapable of adapting to dynamic industrial conditions, creating an urgent need for intelligent, real-time hazard detection.This paper presents an Embedded Intelligent System that integrates Internet of Things (IoT) technology with machine learning (ML) for continuous and proactive industrial safety monitoring. The system employs a multi-sensor array measuring temperature, gas concentration, vibration, and humidity, feeding live data to a Raspberry Pi 3 model B+ which runs a pre-trained ML model for real-time hazard classification at the edge. This edge-based inference eliminates dependence on cloud connectivity, ensuring low-latency detection even in network-constrained environments. To safeguard data integrity and prevent unauthorized access, a multi-layer security framework is implemented, incorporating device authentication, AES-based payload encryption, and TLS-secured MQTT communication.Experimental results confirm that the system achieves 98% accuracy on static benchmark data and 97% accuracy under real-time deployment, demonstrating strong generalizability to live industrial conditions. The proposed system provides a scalable, cost-effective, and intelligent alternative to legacy monitoring infrastructure, with direct applicability across manufacturing, chemical processing, oil and gas, and mining sectors.