EvoLynx: Evolving Conveyor Automation with Adaptive IoT Nerve System

On factory shop floors, in the context of Industry 4.0, there are growing problems of unplanned downtime, energy wastage, and inconsistent motor operation with resultant overall productivity loss. The work discusses a suggested smart and scalable IoT-based conveyor motor automation system relying on real-time monitoring and machine learning-based predictive maintenance, which aims to improve operational efficiency and dependability. The approach involves the utilization of an ESP8266 microcontroller with ACS712 (current), LM35 (temperature), and SW-420 (vibration) sensors to monitor motor health in real-time. The information is transferred wirelessly to Blynk and Power BI dashboards for simple visualization, fault detection, and remote monitoring. Historical sensor data are employed to train a Random Forest Regression model, which forecasts likely failures and performance trends. The core results are unscheduled downtimes reduced, energy used more efficiently, and system responsiveness improved. The novelty of the system is the harmonious combination of IoT sensing, cloud-based analytics, and machine learning algorithms into an integrated, low-latency platform. This feature makes it very suitable and practical for deployment in large-scale smart factories, thus making proactive maintenance and optimal motor utilization possible with minimal human intervention.