Cover Image

Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach

Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guide the moving parts of the machine and reduce friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the be...

Full description

Saved in:
Bibliographic Details
Main Author: Omar, Noraimi
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2018
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://utpedia.utp.edu.my/19310/1/Noraimi%20Omar_20383_Dissertation.pdf
http://utpedia.utp.edu.my/19310/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utp-utpedia.19310
record_format eprints
spelling my-utp-utpedia.193102019-06-10T09:18:31Z http://utpedia.utp.edu.my/19310/ Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach Omar, Noraimi TJ Mechanical engineering and machinery Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guide the moving parts of the machine and reduce friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This research presents a detailed detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott-Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. An algorithm is developed for abnormal condition detection and diagnostics using intelligent systems. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. The outcome from this project would lead to development of an easy to use tool for bearing fault detection and diagnostics. Universiti Teknologi PETRONAS 2018-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19310/1/Noraimi%20Omar_20383_Dissertation.pdf Omar, Noraimi (2018) Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach. Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Omar, Noraimi
Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
description Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guide the moving parts of the machine and reduce friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This research presents a detailed detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott-Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. An algorithm is developed for abnormal condition detection and diagnostics using intelligent systems. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. The outcome from this project would lead to development of an easy to use tool for bearing fault detection and diagnostics.
format Final Year Project
author Omar, Noraimi
author_facet Omar, Noraimi
author_sort Omar, Noraimi
title Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
title_short Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
title_full Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
title_fullStr Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
title_full_unstemmed Antifriction Bearing Malfunction Detection and Diagnostics using Hybrid Approach
title_sort antifriction bearing malfunction detection and diagnostics using hybrid approach
publisher Universiti Teknologi PETRONAS
publishDate 2018
url http://utpedia.utp.edu.my/19310/1/Noraimi%20Omar_20383_Dissertation.pdf
http://utpedia.utp.edu.my/19310/
_version_ 1739832616100560896
score 13.211869

Similar Items