IIoT Machine Health Monitoring Models for Education and Training

*Ishwar Singh, Sean Hodgins, Anoop Gadhrri and Reiner Schmidt*

## **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.

**Keywords:** IoT and IIoT, Machine Health Monitoring, Neural Network, Predictive Maintenance, Wireless Monitoring

#### **1. Introduction**

To improve safety and performance, manufacturing companies have been considering the adoption of advanced technologies, as stipulated in Industry 4.0 reports. These technologies include IoT/IIoT, big data and analytics, smart factory, cyber-physical systems (CPS), and interoperability of automation equipment. Over the last few years Artificial Intelligence (AI) has comeback with a vengeance not seen before in any of modern technology implementations. AI and IoT/IIoT are driving forces in Industry 4.0 with applications in manufacturing, oil and gas, utilities, banking, aerospace and defense, healthcare, retail, telecommunications, smart cities, and transportation. Artificial intelligence's impact on manufacturing can be organized into the following main areas: product quality and yield, predictive maintenance, collaborative robots, generative design, supply chain management and safer work environment.

While there are a lot of papers written about predictive maintenance and AI applications [1–7], but there is a lack of machine health predictive maintenance teaching and training models for university level courses as well as facilities for providing hand-on interactive experiences in the use of IoT/IIoT and Industry 4.0 platforms. To address this need SEPT created a learning factory and a framework for learning these technologies as well as designed and developed machine health monitoring models.

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.

## **2. SEPT learning factory**

The engineering education is witnessing revolutionary changes in response to the huge demand for engineers with high industry 4.0 competencies. Engineering graduates will need to learn IoT and IIoT as the foundation for implementing Industry 4.0 concepts in industrial operations. The school of Engineering Practice and Technology (SEPT) at McMaster University has recently made huge effort to integrate IoT, IIoT and Industry 4.0 in the undergraduate and graduate curriculum [8].

In the undergraduate Automation Engineering Bachelor of Technology offered by SEPT, a new smart systems specialization is introduced in the fourth year, where the offered courses focus on IoT. The other option is the Industrial Automation specialization. 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 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]. The Centre includes a series of specialized learning labs and the SEPT Learning Factory that allow the development of various theoretical and technical skills needed for product production. The Learning Centre complements students' qualifications and abilities by providing new technical skills that emphasize the inherent multidisciplinary nature of smart systems and advanced manufacturing.

#### **2.1 Machine health monitoring**

Machine health monitoring is a key opportunity to improving and maintaining profit. In manufacturing industries, it is expected that when a machine is started it should perform as designed and used for an application and run for hours, months and years without any breakdown interruptions. Vibration monitoring along with power and sound monitoring are a significant source of machine health information. By adopting the use of new technologies such as IIoT can lead to improved manufacturing productivity with more reliability and even reduce skill requirement needs. However, developing and implementing these applications does require a new breed of engineering technology graduates. IIoT offers an opportunity for ubiquitous detection of machinery faults that can lead to prescriptive maintenance plans.

During operation mechanical faults in machines produce unique vibrations which depend upon the geometry of the machine elements such as shaft, spindle etc., and shaft rotation speed, in addition to the obvious load factor. There is a huge list of mechanical faults that can be detected with vibration data collection and performing an analysis on it. This list includes imbalance; misalignment; bent shaft; rubbing shaft; bearing defects; loose parts; and belt drive faults etc. For example, recently a pump servicing company has identified a few major causes of vibration

[12]. Their report lists six main causes of pump vibration problems and anyone of those could take a pump out of service for unplanned and expensive repairs. A pump's poor performance can be due to one of the following vibration problems: pump cavitation; bent pump shaft; pump flow pulsation; pump impeller imbalance; pump bearing issues; and misalignment of the shaft.
