TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

Area of Science:

• Industrial Engineering
• Computer Science
• Artificial Intelligence

Background:

• Industry 4.0, AI, and IoT are driving industrial process digitization and automation.
• Predictive maintenance offers significant advantages over traditional methods, including reduced downtime and costs.
• Current industrial maintenance strategies can be enhanced through advanced computational approaches.

Purpose of the Study:

• To introduce TIP4.0, a modular edge computing platform for predictive maintenance.
• To demonstrate the platform's adaptability to various hardware and deployment scenarios.
• To validate the effectiveness of TIP4.0 using real-world data and machine learning.

Main Methods:

• Development of TIP4.0, a modular software solution for edge computing gateways, built on Yocto.
• Integration of sensor networks and modular software for industrial environments.
• Validation using Commercial Off-the-Shelf (COTS) hardware and a public dataset with a Convolutional Neural Network (CNN) architecture.

Main Results:

• The TIP4.0 platform demonstrated competitive performance in predictive maintenance scenarios.
• CNN inference on the TIP4.0 platform was significantly faster than uncompressed models on CPU and GPU.
• The study highlighted the potential of distributed edge computing in industrial settings.

Conclusions:

• TIP4.0 offers a robust and adaptable solution for implementing predictive maintenance in Industry 4.0.
• Edge computing combined with AI, particularly CNNs, can optimize industrial maintenance operations.
• The platform facilitates improved equipment effectiveness, reduced costs, and enhanced operational efficiency.