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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

DL for predicting RLU

As we have discussed, we are trying to calculate the amount of time before an engine needs maintenance. What makes this dataset special is that the engines run all the way until failure, giving us the precise RLU information for every engine at every point in time.

Calculating cut-off times

Let's consider the FD004 dataset, that contains as much as 249 engines (engine_no) monitored over time (time_in_cycles). Each engine has operational_settings and sensor_measurements recorded for each cycle:

data_path = 'train_FD004.txt'
data = utils.load_data(data_path)

To train a model that will predict RUL, we can simulate real predictions by choosing a random point in the life of the engine and only...

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