A Novel Framework for Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Coefficients of Walsh Transform and Cosine Linear Discriminant Analysis

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the p...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.150128-150141
Main Authors: Ahmad, Zahoor, Rai, Akhand, Hasan, Md Junayed, Kim, Cheol Hong, Kim, Jong-Myon
Format: Article
Language:English
Subjects:
Centrifugal pump
Centrifugal pumps
Coefficients
cosine linear discriminant analysis
Discriminant analysis
Fault diagnosis
Feature extraction
K-nearest neighbors algorithm
Linear discriminant analysis
Principal component analysis
Pumps
Structural vibration
Transforms
Vibrations
walsh transform
Walsh transforms
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Summary:In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the proposed method selects the fault characteristic coefficients of the Walsh spectrum. In the second stage, statistical features in the time and Walsh spectrum domain are extracted from the selected fault characteristic coefficients of the Walsh transform. These extracted raw statistical features result in a hybrid high-dimensional space. Not all these extracted features help illustrate the condition of the centrifugal pump. To overcome this issue, novel cosine linear discriminant analysis is introduced in the third stage. Cosine linear discriminant analysis is a dimensionality reduction technique which selects similar interclass features and adds them to the illustrative feature pool, which contains key discriminant features that represent the condition of the centrifugal pump. To achieve maximum between-class separation, linear discriminant analysis is then applied to the illustrative feature pool. This combination of illustrative feature pool creation and linear discriminant analysis forms the proposed application of cosine linear discriminant analysis. The reduced discriminant feature set obtained from cosine linear discriminant analysis is then given as an input to the K-nearest neighbor classifier for classification. The classification results obtained from the proposed method outperform the previously presented state-of-the-art methods in terms of fault classification accuracy.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3124903