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

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 [Research Reports] (Unpublished)

Abstract

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.

Metadata

Item Type: Research Reports
Creators:
Creators
Email / ID Num.
Othman, Nor Azlan
UNSPECIFIED
Damanhuri, Nor Salwa
UNSPECIFIED
Hamzah, Norhazimi
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Production of electric energy or power. Powerplants. Central stations
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus
Keywords: Rotating Machinery, Power Spectrum Density (PSD), Principal Component Analysis (PCA)
Date: December 2009
URI: https://ir.uitm.edu.my/id/eprint/42033
Edit Item
Edit Item

Download

[thumbnail of 42033.pdf] Text
42033.pdf

Download (40kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number

42033

Indexing

Downloads per month over past year

View more statistics

Origin of downloads