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Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

Sensors (Basel, Switzerland), 2021-09, Vol.21 (18), p.6247 [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/s21186247 ;PMID: 34577453

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Title:
Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump
Author: Konieczny, Jarosław ; Stojek, Jerzy
Subjects: Accuracy ; Algorithms ; Classification ; classifier ; Classifiers ; Cooperation ; Diagnostic systems ; diagnostics ; Efficiency ; Experiments ; Hydraulics ; K-nearest neighbours ; Kalman filters ; learning system ; Machine learning ; Medical diagnosis ; Model accuracy ; multi-piston pump ; Neural networks ; Positive displacement pumps ; signal analysis ; Time-frequency analysis ; Vibration measurement ; Viscosity ; Wavelet transforms ; Wear
Is Part Of: Sensors (Basel, Switzerland), 2021-09, Vol.21 (18), p.6247
Description: This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s21186247
PMID: 34577453
Source: Open Access: DOAJ Directory of Open Access Journals
Geneva Foundation Free Medical Journals at publisher websites
AUTh Library subscriptions: ProQuest Central
NCBI PubMed Central（免费）
ROAD: Directory of Open Access Scholarly Resources

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Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

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