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Medientyp:
E-Artikel
Titel:
Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump
Beteiligte:
Konieczny, Jarosław;
Stojek, Jerzy
Erschienen:
MDPI AG, 2021
Erschienen in:
Sensors, 21 (2021) 18, Seite 6247
Sprache:
Englisch
DOI:
10.3390/s21186247
ISSN:
1424-8220
Entstehung:
Anmerkungen:
Beschreibung:
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.