Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis

Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis

In this paper, the application of principle component analysis (PCA) as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condit...

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Main Authors: Mohd Fadhlan, Mohd Yusof, C. K. E., Nizwan, S. A., Ong, M. Q. M., Ridzuan
Format: Conference or Workshop Item
Language:English
English
Published: 2016
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/14337/
http://umpir.ump.edu.my/id/eprint/14337/1/Clustering%20of%20Frequency%20Spectrum%20from%20Different%20Bearing%20Fault%20using%20Principle%20Component%20Analysis..pdf
http://umpir.ump.edu.my/id/eprint/14337/7/fkm-2016-M.F.M.Yusof-Clustering%20of%20Frequency.pdf
id ump-14337
recordtype eprints
spelling ump-143372017-03-23T02:10:20Z http://umpir.ump.edu.my/id/eprint/14337/ Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis Mohd Fadhlan, Mohd Yusof C. K. E., Nizwan S. A., Ong M. Q. M., Ridzuan TJ Mechanical engineering and machinery In this paper, the application of principle component analysis (PCA) as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the amplitude of vibration at Ball Passing Frequency Outer Race and Ball Passing Frequency Inner Race will increase in align with the presence of outer race defect and inner race defect respectively. Moreover, the overall amplitude of vibration spectrum was found to be uniformly increased for the case of corroded bearing due to the widespread uniform corrosion on the entire bearing. By applying principle component analysis, the change in amplitude at any of these fundamental frequencies can be detected. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14337/1/Clustering%20of%20Frequency%20Spectrum%20from%20Different%20Bearing%20Fault%20using%20Principle%20Component%20Analysis..pdf application/pdf en http://umpir.ump.edu.my/id/eprint/14337/7/fkm-2016-M.F.M.Yusof-Clustering%20of%20Frequency.pdf Mohd Fadhlan, Mohd Yusof and C. K. E., Nizwan and S. A., Ong and M. Q. M., Ridzuan (2016) Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis. In: 2nd International Conference on Automotive Innovation and Green Energy Vehicle (AIGEV 2016), 2-3 August 2016 , Malaysia Automotive Institute, Cyberjaya, Selangor. pp. 1-10.. (Unpublished)
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mohd Fadhlan, Mohd Yusof
C. K. E., Nizwan
S. A., Ong
M. Q. M., Ridzuan
Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
description In this paper, the application of principle component analysis (PCA) as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the amplitude of vibration at Ball Passing Frequency Outer Race and Ball Passing Frequency Inner Race will increase in align with the presence of outer race defect and inner race defect respectively. Moreover, the overall amplitude of vibration spectrum was found to be uniformly increased for the case of corroded bearing due to the widespread uniform corrosion on the entire bearing. By applying principle component analysis, the change in amplitude at any of these fundamental frequencies can be detected. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification.
format Conference or Workshop Item
author Mohd Fadhlan, Mohd Yusof
C. K. E., Nizwan
S. A., Ong
M. Q. M., Ridzuan
author_facet Mohd Fadhlan, Mohd Yusof
C. K. E., Nizwan
S. A., Ong
M. Q. M., Ridzuan
author_sort Mohd Fadhlan, Mohd Yusof
title Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
title_short Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
title_full Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
title_fullStr Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
title_full_unstemmed Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis
title_sort clustering of frequency spectrum from different bearing fault using principle component analysis
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/14337/
http://umpir.ump.edu.my/id/eprint/14337/1/Clustering%20of%20Frequency%20Spectrum%20from%20Different%20Bearing%20Fault%20using%20Principle%20Component%20Analysis..pdf
http://umpir.ump.edu.my/id/eprint/14337/7/fkm-2016-M.F.M.Yusof-Clustering%20of%20Frequency.pdf
first_indexed 2023-09-18T22:17:59Z
last_indexed 2023-09-18T22:17:59Z
_version_ 1777415465493069824

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