Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/519941
Title: | Investigations on predictive maintenance for automobile industry using edge intelligence and blockchain |
Researcher: | Gopalakrishnan S |
Guide(s): | Senthikumaran M |
Keywords: | Computer Science Computer Science Information Systems Edge Intelligence Engineering and Technology IoT K-Means |
University: | Anna University |
Completed Date: | 2022 |
Abstract: | newlineAs technology advances quickly, businesses are attempting to incorporate it to increase production and efficiency. With the introduction of the fourth industrial revolution, also known as Industry 4.0, the boundaries of industry have extended. Businesses now have a wide range of possibilities in every imaginable way thanks to the integration of IoT and IIoT. It is believed that the predictive maintenance sector could benefit from its implementation. Industry 4.0 uses clever sensors and actuators to help collect a lot of data. These vast data sets can be examined by a variety of machine learning algorithms to yield informative results. Using data/readouts from numerous IIoT instruments, artificial intelligence can forecast the performance and health of a piece of equipment. |
Pagination: | xvii,193 p. |
URI: | http://hdl.handle.net/10603/519941 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 26.03 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 2.35 MB | Adobe PDF | View/Open | |
03_content.pdf | 269.93 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 144.17 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 843.86 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 518.15 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 841.22 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 670.4 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 599.82 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.18 MB | Adobe PDF | View/Open | |
11_annexures.pdf | 1.09 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 119.36 kB | Adobe PDF | View/Open |
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