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Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

Sensors (Basel, Switzerland), 2021-12, Vol.22 (1), p.179 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22010179 ;PMID: 35009719

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  • Title:
    Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis
  • Author: Ahmad, Zahoor ; Nguyen, Tuan-Khai ; Ahmad, Sajjad ; Nguyen, Cong Dai ; Kim, Jong-Myon
  • Subjects: Algorithms ; Background noise ; Centrifugal classifiers ; Centrifugal pumps ; Classification ; Classification (centrifugal) ; Classifiers ; Defects ; Discriminant analysis ; Domains ; Eigenvalues ; Fault diagnosis ; Feature extraction ; Frequencies ; Graph representations ; Infrared analysis ; multistage centrifugal pump ; Noise ; Principal Component Analysis ; Principal components analysis ; Vibration ; Vibration analysis ; Wavelet transforms
  • Is Part Of: Sensors (Basel, Switzerland), 2021-12, Vol.22 (1), p.179
  • Description: This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22010179
    PMID: 35009719
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals

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