Condition classification of heating systems valves based on acoustic features and machine learning

Primož Potočnik (Author), Borja Olmos Lopez-Roso (Author), Lučka Vodopivec (Author), Egon Susič (Author), Edvard Govekar (Author)

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.

Keywords

valves;district heating;acoustic features;feature selection;classification;machine learning;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	628.8:534(045)
COBISS:	35370243
ISSN:	0003-682X
Views:	443
Downloads:	104
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	ventili;daljinsko ogrevanje;akustične značilke;izbira značilk;razvrščanje;strojno učenje;
Type (COBISS):	Article
Embargo end date (OpenAIRE):	2022-11-03
Pages:	str. 1-9
Issue:	ǂVol. ǂ174
Chronology:	Mar. 2021
DOI:	10.1016/j.apacoust.2020.107736
ID:	12123735