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Condition classification of heating systems valves based on acoustic features and machine learning
ID Potočnik, Primož (Author), ID Olmos Lopez-Roso, Borja (Author), ID Vodopivec, Lučka (Author), ID Susič, Egon (Author), ID Govekar, Edvard (Author)

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Abstract
The quality and condition of valves installed in district heating systems can be reflected by the soundsemitted. In this paper, a framework for a systematic approach towards the classification of valve soundsis proposed, based on acoustic features and machine learning models. The methods include the extractionof spectral and psychoacoustic features, alongside the application of a wrapper-based feature selectionmethod which, when combined with machine learning models, simultaneously selects the most informa-tive features and builds optimal classification models. The maximal balanced classification rate (BCR) wasused as the optimisation criterion in this study. Results demonstrate that the specific valve conditions canbe correctly classified with a high BCR as follows: cavitation BCR = 1, whistling BCR = 0.978, and rattlingBCR = 1. The proposed framework for a wrapper-based selection of informative features and correspond-ing machine learning models confirms the usefulness of psychoacoustic features and machine learningmodels for the classification of valve conditions. The proposed framework is, however, general and canbe applied to various acoustic-based industrial condition monitoring challenges.

Language:English
Keywords:valves, district heating, acoustic features, feature selection, classification, machine learning
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication status:Published
Publication version:Author Accepted Manuscript
Year:2020
Number of pages:Str. 1-9
Numbering:Vol. 174
PID:20.500.12556/RUL-121854 This link opens in a new window
UDC:628.8:534(045)
ISSN on article:0003-682X
DOI:10.1016/j.apacoust.2020.107736 This link opens in a new window
COBISS.SI-ID:35370243 This link opens in a new window
Publication date in RUL:03.11.2020
Views:789
Downloads:180
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Record is a part of a journal

Title:Applied acoustics
Shortened title:Appl. acoust.
Publisher:Elsevier
ISSN:0003-682X
COBISS.SI-ID:24982016 This link opens in a new window

Secondary language

Language:Slovenian
Keywords:ventili, daljinsko ogrevanje, akustične značilke, izbira značilk, razvrščanje, strojno učenje

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P2-0241
Name:Sinergetika kompleksnih sistemov in procesov

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