Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis
This paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectivenes...
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2013-01-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1155/2013/625863 |
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doaj-d05ad004842c44cd9b74b5c0dd9b61282020-11-25T03:44:06ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322013-01-01510.1155/2013/62586310.1155_2013/625863Detection of Early Faults in Rotating Machinery Based on Wavelet AnalysisMeng Hee Lim0M. S. Leong1 Razak School of Engineering & Advanced Technology, Universiti Teknologi Malaysia International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor, MalaysiaThis paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectiveness to detect machinery faults at the early stage was evaluated based on signal simulation and experimental study. The proposed method provides a more standardised approach to visualise the current state of rotor dynamics of a rotating machinery by taking into account the effects of time shift, wavelet edge distortion, and system noise suppression. The experimental results showed that this method is able to reveal subtle changes of the vibration signal characteristics in both the frequency content distribution and the amplitude distortion caused by minor rotor unbalance and blade rubbing conditions. Besides, this method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis.https://doi.org/10.1155/2013/625863 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Meng Hee Lim M. S. Leong |
spellingShingle |
Meng Hee Lim M. S. Leong Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis Advances in Mechanical Engineering |
author_facet |
Meng Hee Lim M. S. Leong |
author_sort |
Meng Hee Lim |
title |
Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis |
title_short |
Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis |
title_full |
Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis |
title_fullStr |
Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis |
title_full_unstemmed |
Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis |
title_sort |
detection of early faults in rotating machinery based on wavelet analysis |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8132 |
publishDate |
2013-01-01 |
description |
This paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectiveness to detect machinery faults at the early stage was evaluated based on signal simulation and experimental study. The proposed method provides a more standardised approach to visualise the current state of rotor dynamics of a rotating machinery by taking into account the effects of time shift, wavelet edge distortion, and system noise suppression. The experimental results showed that this method is able to reveal subtle changes of the vibration signal characteristics in both the frequency content distribution and the amplitude distortion caused by minor rotor unbalance and blade rubbing conditions. Besides, this method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis. |
url |
https://doi.org/10.1155/2013/625863 |
work_keys_str_mv |
AT mengheelim detectionofearlyfaultsinrotatingmachinerybasedonwaveletanalysis AT msleong detectionofearlyfaultsinrotatingmachinerybasedonwaveletanalysis |
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