IIoT-Based Monitoring and Predictive Maintenance of Industrial Machine Tools Using CtrlX Automation Platform

Abstract

The emergence of the Industrial Internet of Things (IIoT) is transforming traditional industrial maintenance into a predictive, data-driven process. This paper presents a comprehensive architecture for an IIoT-enabled monitoring and maintenance system focused on industrial machine tools. Leveraging proximity sensors to capture rotation speed, axial movement, and tool position, the system integrates sensor data with microprocessors such as Arduino STM32 and PID controllers to ensure real-time control and analysis. The architecture utilizes both local processing through Arduino IDE and centralized visualization via Node-RED, enabling efficient data transmission to cloud services through an Internet gateway. Key technologies such as 5G communication, big data analytics, and digital twins are incorporated to enhance predictive maintenance capabilities, reduce downtime, and improve overall equipment effectiveness (OEE). Despite the promise of IIoT integration, challenges such as interoperability, cybersecurity, and legacy system adaptation remain significant. This study proposes a scalable and intelligent CPS framework to overcome these obstacles and highlights the potential of Machine Tool 4.0 in achieving smarter, more autonomous, and sustainable manufacturing ecosystems.