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Hands-On Deep Learning for IoT

You're reading from   Hands-On Deep Learning for IoT Train neural network models to develop intelligent IoT applications

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Length 308 pages
Edition 1st Edition
Languages
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Authors (3):
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Aditya Trivedi Aditya Trivedi
Author Profile Icon Aditya Trivedi
Aditya Trivedi
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Author Profile Icon Dr. Mohammad Abdur Razzaque
Dr. Mohammad Abdur Razzaque
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks FREE CHAPTER
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Predictive Maintenance for IoT

In Internet of Things (IoT) devices, streaming data is generated for one event at a time. DL-based approaches can examine this data in order to diagnose the problem across the fleet in real time, and the future health of individual units can be predicted in order to enable on-demand maintenance. This strategy is known as predictive (or condition-based) maintenance. This approach is now emerging as one of the most promising and lucrative industrial applications of the IoT.

Considering these motivations, in this chapter, we will look at how to develop a DL solution for predictive maintenance for IoT using the Turbofan Engine Degradation Simulation dataset. The idea behind predictive maintenance is to determine whether the failure patterns of various types can be predictable. Furthermore, we will discuss how to collect data from IoT-enabled devices...

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