Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms
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Abstract
Industrial Internet of Things (IIoT) is using Internet of Things (IoT) technologies to a vast extent in different industries across all verticals. Especially in manufacturing industries, there is a great boom in using IIOT because manufacturing industry has seen a great potential in it, and they are heavily investing on it. Manufacturing industry is one such industry where physical devices are used and are important for production; there are no other alternatives yet. These physical devices are a very crucial part of the manufacturing industry because if there is any machine failure at any time, then it is a huge loss for the company in terms of revenue. Not able to fulfil their commitment will also be a huge loss in terms of brand value. In this competitive market where there are a number of organizations, no organization would want to compromise their brand value and profit because both go hand in hand. Therefore, in this chapter, we will be exploring and finding suitable ways where AI and IIoT can be helpful for the organizations to overcome these challenges, and it will surely become a sustainable growth path for organizations in all industries where physical devices’ failure is cost high. This chapter mainly revolves around how sensors can be used to collect data from physical devices and how this data is moved over the internet to cloud, how deep learning model runs every time on that real-time collected data to predict the probability of machine failure, and how it raises alerts to concerned people if there is a high probability of machine failure. This system has only one challenge, which is latency, and this can be resolve using fog computing.
Abstract
Industrial Internet of Things (IIoT) is using Internet of Things (IoT) technologies to a vast extent in different industries across all verticals. Especially in manufacturing industries, there is a great boom in using IIOT because manufacturing industry has seen a great potential in it, and they are heavily investing on it. Manufacturing industry is one such industry where physical devices are used and are important for production; there are no other alternatives yet. These physical devices are a very crucial part of the manufacturing industry because if there is any machine failure at any time, then it is a huge loss for the company in terms of revenue. Not able to fulfil their commitment will also be a huge loss in terms of brand value. In this competitive market where there are a number of organizations, no organization would want to compromise their brand value and profit because both go hand in hand. Therefore, in this chapter, we will be exploring and finding suitable ways where AI and IIoT can be helpful for the organizations to overcome these challenges, and it will surely become a sustainable growth path for organizations in all industries where physical devices’ failure is cost high. This chapter mainly revolves around how sensors can be used to collect data from physical devices and how this data is moved over the internet to cloud, how deep learning model runs every time on that real-time collected data to predict the probability of machine failure, and how it raises alerts to concerned people if there is a high probability of machine failure. This system has only one challenge, which is latency, and this can be resolve using fog computing.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributing authors IX
- Chapter 1 Digital transformation technology and tools: shaping the future of primary health care 1
- Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms 27
- Chapter 3 A deep survey on quantum computing technologies 49
- Chapter 4 Machine learning and deep learning 71
- Chapter 5 From evolution to revolution: the contemporary development of quantum computing 85
- Chapter 6 Real-time big data analytics 107
- Chapter 7 Quantum processors/networks/sensors 129
- Chapter 8 Quantum computing in automata theory 147
- Chapter 9 Quantum computing: future of artificial intelligence and its applications 163
- Chapter 10 A leap among quantum ML and DL models: a review 185
- Chapter 11 A perspective study on quantum machine learning models for the areas of medicine, materials, sensing, and communication 205
- Chapter 12 Quantum computing: application-specific need of the hour 225
- Chapter 13 Industrial Internet of things and Industry 4.0: a learner’s perspectives toward quantum technologies 243
- Chapter 14 Applications of quantum AI for healthcare 271
- Biography 289
- Index 291
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributing authors IX
- Chapter 1 Digital transformation technology and tools: shaping the future of primary health care 1
- Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms 27
- Chapter 3 A deep survey on quantum computing technologies 49
- Chapter 4 Machine learning and deep learning 71
- Chapter 5 From evolution to revolution: the contemporary development of quantum computing 85
- Chapter 6 Real-time big data analytics 107
- Chapter 7 Quantum processors/networks/sensors 129
- Chapter 8 Quantum computing in automata theory 147
- Chapter 9 Quantum computing: future of artificial intelligence and its applications 163
- Chapter 10 A leap among quantum ML and DL models: a review 185
- Chapter 11 A perspective study on quantum machine learning models for the areas of medicine, materials, sensing, and communication 205
- Chapter 12 Quantum computing: application-specific need of the hour 225
- Chapter 13 Industrial Internet of things and Industry 4.0: a learner’s perspectives toward quantum technologies 243
- Chapter 14 Applications of quantum AI for healthcare 271
- Biography 289
- Index 291
