Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis
In this paper, the features of vibration signals from normal and faulty conditions of a centrifugal pump were extracted from time-domain data using the discrete wavelet transform (DWT). The DWT with Multi Resolution Analysis (MRA) was used to pre-process raw vibration signals prior to extraction of...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
Kluwer Academic Publishers
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/22246 |
| _version_ | 1848750816831209472 |
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| author | Kamiel, Berli McKee, Kristoffer Entwistle, Rodney Mazhar, Ilyas Howard, Ian |
| author_facet | Kamiel, Berli McKee, Kristoffer Entwistle, Rodney Mazhar, Ilyas Howard, Ian |
| author_sort | Kamiel, Berli |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, the features of vibration signals from normal and faulty conditions of a centrifugal pump were extracted from time-domain data using the discrete wavelet transform (DWT). The DWT with Multi Resolution Analysis (MRA) was used to pre-process raw vibration signals prior to extraction of statistical features. The features obtained were used as input to Principal Component Analysis (PCA). A method based on PCA was then developed to build a framework for multi-fault diagnosis of centrifugal pumps by using historical normal conditions. The fault detection was determined using T 2 -statistics and Q-statistics while fault identification was carried out through the combination of loadings and scores of principal components (PCs). The normal and faulty conditions of the centrifugal pump were collected from the Spectra Quest Machinery Fault Simulator. Various fault conditions were investigated in the experiment including cavitation, impeller fault, and combination of impeller fault and cavitation. The results showed that combined wavelet-PCA can be used to detect multi-faults in the centrifugal pump. Furthermore, the combination of loadings and scores of PCs was demonstrated which showed effective fault identification. |
| first_indexed | 2025-11-14T07:42:51Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22246 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:42:51Z |
| publishDate | 2015 |
| publisher | Kluwer Academic Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-222462020-07-27T03:08:05Z Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis Kamiel, Berli McKee, Kristoffer Entwistle, Rodney Mazhar, Ilyas Howard, Ian In this paper, the features of vibration signals from normal and faulty conditions of a centrifugal pump were extracted from time-domain data using the discrete wavelet transform (DWT). The DWT with Multi Resolution Analysis (MRA) was used to pre-process raw vibration signals prior to extraction of statistical features. The features obtained were used as input to Principal Component Analysis (PCA). A method based on PCA was then developed to build a framework for multi-fault diagnosis of centrifugal pumps by using historical normal conditions. The fault detection was determined using T 2 -statistics and Q-statistics while fault identification was carried out through the combination of loadings and scores of principal components (PCs). The normal and faulty conditions of the centrifugal pump were collected from the Spectra Quest Machinery Fault Simulator. Various fault conditions were investigated in the experiment including cavitation, impeller fault, and combination of impeller fault and cavitation. The results showed that combined wavelet-PCA can be used to detect multi-faults in the centrifugal pump. Furthermore, the combination of loadings and scores of PCs was demonstrated which showed effective fault identification. 2015 Conference Paper http://hdl.handle.net/20.500.11937/22246 10.1007/978-3-319-06590-8_45 Kluwer Academic Publishers restricted |
| spellingShingle | Kamiel, Berli McKee, Kristoffer Entwistle, Rodney Mazhar, Ilyas Howard, Ian Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title | Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title_full | Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title_fullStr | Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title_full_unstemmed | Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title_short | Multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| title_sort | multi fault diagnosis of the centrifugal pump using the wavelet transform and principal component analysis |
| url | http://hdl.handle.net/20.500.11937/22246 |