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Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.

The aim of this paper is to diagnose the faults that occurred in rotating machinery. Pattern recognition technique was implemented using three main steps of fault diagnosis; feature extraction, dimensionality reduction and fault classification. This paper focuses on the faulty bearing which mainly c...

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Main Authors: Othman, Nor Azlan, Damanhuri, Nor Salwa, Hamzah, Norhazimi
Format: Research Reports
Language:English
Published: 2009
Subjects:
TK Electrical engineering. Electronics. Nuclear engineering
Production of electric energy or power. Powerplants. Central stations
Online Access:http://ir.uitm.edu.my/id/eprint/42033/1/42033.pdf
http://ir.uitm.edu.my/id/eprint/42033/
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spelling my.uitm.ir.420332021-02-19T01:23:22Z http://ir.uitm.edu.my/id/eprint/42033/ Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah. Othman, Nor Azlan Damanhuri, Nor Salwa Hamzah, Norhazimi TK Electrical engineering. Electronics. Nuclear engineering Production of electric energy or power. Powerplants. Central stations The aim of this paper is to diagnose the faults that occurred in rotating machinery. Pattern recognition technique was implemented using three main steps of fault diagnosis; feature extraction, dimensionality reduction and fault classification. This paper focuses on the faulty bearing which mainly caused by mass imbalance and axis misalignment. Vibration signal that obtained from the rotating machinery is extracted by using non-parametric or parametric method to get the power spectrum density (PSD). Principal Component Analysis (PCA) is then introduced to reduce the complexity as well as smooth the classification process. By analyzing the vibration signal obtained from the test rigs (rigs that are built to demonstrate the effect of faults in rotating machinery), it gives solid information concerning any faults within the rotating machinery. 2009-12 Research Reports NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/42033/1/42033.pdf Othman, Nor Azlan and Damanhuri, Nor Salwa and Hamzah, Norhazimi (2009) Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah. [Research Reports] (Unpublished)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic TK Electrical engineering. Electronics. Nuclear engineering
Production of electric energy or power. Powerplants. Central stations
spellingShingle TK Electrical engineering. Electronics. Nuclear engineering
Production of electric energy or power. Powerplants. Central stations
Othman, Nor Azlan
Damanhuri, Nor Salwa
Hamzah, Norhazimi
Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
description The aim of this paper is to diagnose the faults that occurred in rotating machinery. Pattern recognition technique was implemented using three main steps of fault diagnosis; feature extraction, dimensionality reduction and fault classification. This paper focuses on the faulty bearing which mainly caused by mass imbalance and axis misalignment. Vibration signal that obtained from the rotating machinery is extracted by using non-parametric or parametric method to get the power spectrum density (PSD). Principal Component Analysis (PCA) is then introduced to reduce the complexity as well as smooth the classification process. By analyzing the vibration signal obtained from the test rigs (rigs that are built to demonstrate the effect of faults in rotating machinery), it gives solid information concerning any faults within the rotating machinery.
format Research Reports
author Othman, Nor Azlan
Damanhuri, Nor Salwa
Hamzah, Norhazimi
author_facet Othman, Nor Azlan
Damanhuri, Nor Salwa
Hamzah, Norhazimi
author_sort Othman, Nor Azlan
title Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
title_short Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
title_full Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
title_fullStr Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
title_full_unstemmed Fault diagnosis using feature extraction in power plant rotating machinery / Nor Azlan Othman, Nor Salwa Damanhuri and Norhazimi Hamzah.
title_sort fault diagnosis using feature extraction in power plant rotating machinery / nor azlan othman, nor salwa damanhuri and norhazimi hamzah.
publishDate 2009
url http://ir.uitm.edu.my/id/eprint/42033/1/42033.pdf
http://ir.uitm.edu.my/id/eprint/42033/
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score 13.211869

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