Please use this identifier to cite or link to this item:
Title: Defects detection using machine learning in condition monitoring
Authors: Tan, Jian Jia
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A2163-191
Abstract: This final year project aims to make use of machine learning technique in condition monitoring for defects detection on the third rail. The machine learning model taken into consideration is the Support Vector Machine. The critical features were extracted from the data collected. This report analyses how the defects detection of the third rail can be achieved by using the Support Vector Machine technique.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
fyp final report.pdf
  Restricted Access
2.14 MBAdobe PDFView/Open

Page view(s)

Updated on Nov 25, 2022

Download(s) 50

Updated on Nov 25, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.