Abstract: Contemporary industrial environments demand continuous and intelligent supervision of operational equipment to ensure uninterrupted productivity, personnel safety, and optimized resource utilization. Conventional maintenance paradigms, which rely predominantly on periodic manual inspection, have proven inadequate for the dynamic and high-throughput demands of modern manufacturing facilities. The emergence of Internet of Things (IoT) technology has catalyzed a fundamental transformation in industrial monitoring by enabling autonomous, wireless, and real-time acquisition of critical machine parameters. This paper presents the design, development, and experimental validation of an IoT-based Industrial Machine Health Monitoring and Automated Control System constructed around the ESP32 microcontroller. The proposed architecture integrates a heterogeneous array of sensors—encompassing temperature (DHT11), vibration, smoke/gas, voltage, and current transducers—to facilitate holistic and multi-dimensional assessment of machine operational health. Sensor data is continuously acquired, digitally processed, and securely transmitted to the Blynk cloud platform via Wi-Fi, enabling remote visualization and analytics through mobile and web interfaces. A hierarchical alert and protection framework is embedded within the system, capable of triggering auditory alarms, dispatching cloud-based notifications, and executing automatic relay-controlled disconnection of machine power circuits upon detection of anomalous operating conditions. Experimental results substantiate the system's capability to deliver accurate, low-latency monitoring alongside dependable protective responses across diverse fault scenarios. The proposed solution offers a scalable, cost-effective, and energy-efficient alternative to conventional industrial monitoring approaches, with direct applicability in predictive maintenance, fault diagnostics, and smart factory integration.

Keywords: Internet of Things (IoT), ESP32 Microcontroller, Industrial Machine Health Monitoring, Predictive Maintenance, Blynk Cloud Platform, Vibration Analysis, Temperature Monitoring, Remote Condition Monitoring, Industrial Automation, Smart Factory.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14545