Abstract
IoT, IIoT and Industry 4.0 technologies are leading the way for digital transformation in manufacturing, healthcare, transportation, energy, retail, cities, supply chain, agriculture, buildings, and other sectors. Machine health monitoring and predictive maintenance of rotating machines is an innovative IIoT use case in the manufacturing and energy sectors. This chapter covers how machine health monitoring can be implemented using advanced sensor technology as a basis for predictive maintenance in rotating devices. It also covers how sensor data can be collected from the devices at the edge, preprocessed in a microcontroller/edge node, and sent to the cloud or local server for advanced data intelligence. In addition, this chapter describes the design and operation of three innovative models for education and training supporting the accelerated adoption of these technologies in industry sectors.
Highlights
To improve safety and performance, manufacturing companies have been considering the adoption of advanced technologies, as stipulated in Industry 4.0 reports
To reinforce hands-on learning two key aspects of machine health monitoring are presented in this chapter: foundational technologies such as sensors for predictive maintenance, IoT and IIoT ecosystem, and cyber-physical systems (CPS) monitoring tools; the second aspect covers in detail the design, development, and implementation of machine health learning models along with implementation of Artificial Intelligence (AI) tools for real-time data generation, preprocessing it and sending to the cloud or local server for data analytics and visualization
A new introductory IoT course (SMRTTECH 3CC3) was developed and offered for the first time at the 3A level in the fall of 2019. The purpose of this course is to introduce the students to the fascinating world of IoT before choosing their specialization for the fourth year and before going to their mandatory co-op training [9]. Another effort by school of Engineering Practice and Technology (SEPT) is the formation of a Cyber-Physical Systems Learning Centre that focuses on implementing Industry 4.0 concepts for teaching, training, and research at McMaster University [10, 11]
Summary
To improve safety and performance, manufacturing companies have been considering the adoption of advanced technologies, as stipulated in Industry 4.0 reports. To reinforce hands-on learning two key aspects of machine health monitoring are presented in this chapter: foundational technologies such as sensors for predictive maintenance, IoT and IIoT ecosystem, and CPS monitoring tools; the second aspect covers in detail the design, development, and implementation of machine health learning models along with implementation of AI tools for real-time data generation, preprocessing it and sending to the cloud or local server for data analytics and visualization. These models provide an opportunity for developing and learning multidisciplinary and multi-capability skills in a laboratory setting
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