**6. Conclusion**

The term IoT was coined in 1999 and it led to the evolution of IIoT enabled by other technologies that were being developed independently of each other. These technologies include such as: cyber-physical systems & cybersecurity; edge and cloud computing; mobile technologies; machine-to-machine communication; 3D printing; advanced robotics; big data; RFID technology; and AI. The SEPT Learning Factory is state-of-the art facility at McMaster University for the demonstration of integration of these technologies as well as development of educational/training material and resources, new technologies, and applied research.

Based on AI modeling the Fan Fault Detection and Diagnosis System described is able determine a fault state that was not included in training the AI model. The IIoT Vibration Demonstration Station using the neural networking model can detect the machine fault in real-time and publish the outcome on a dashboard connected via MQTT platform. The Machine Health Monitoring and Prediction Platform on the

#### *IoT Applications Computing*

other hand combines the best features of the last two models for advanced predictive maintenance learning concepts and real-time demonstrations. This station has been developed, with an embedded vibration sensor and other sensors, that analyses a system for faults and transmits information wirelessly. Students will be able to report errors such as general imbalances, mechanical failure, resonance, electrical faults, and critical speeds. It can be easily extended to further application demonstrations such as bearing faults, AI modeling using multiple sensor inputs and analysis.

In this chapter we have described IIoT machine health monitoring foundations, models and applications for education and training. We have also illustrated how these models can be extended for development of predictive maintenance using AI technology.
