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https://hdl.handle.net/10356/148984
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ng, Zhi Sheng | en_US |
dc.date.accessioned | 2021-05-21T12:52:09Z | - |
dc.date.available | 2021-05-21T12:52:09Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Ng, Z. S. (2021). Machine learning with DSP for condition monitoring system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148984 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/148984 | - |
dc.description.abstract | Condition monitoring is the process of monitoring a parameter of condition in a system in order to identify a significant change which is indicative of a developing fault. It has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure. Using machine learning techniques, the big data gathered around a system can be analysed as a single coherent whole to draw conclusions about its current state of health. This project will develop a condition monitoring method using machine learning to detect defects on a real life system. A test jig will be used to mimic a real life system to collect sufficient data for machine learning. A DSP will be used to implement the machine learning algorithm. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Nanyang Technological University | en_US |
dc.subject | Engineering::Electrical and electronic engineering | en_US |
dc.title | Machine learning with DSP for condition monitoring system | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | See Kye Yak | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Bachelor of Engineering (Electrical and Electronic Engineering) | en_US |
dc.contributor.supervisoremail | EKYSEE@ntu.edu.sg | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
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FYP Report (Project No_ A2185-201).pdf Restricted Access | 2.25 MB | Adobe PDF | View/Open |
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