20.500.12556/RUL-121854 Condition classification of heating systems valves based on acoustic features and machine learning 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. valves district heating acoustic features feature selection classification machine learning ventili daljinsko ogrevanje akustične značilke izbira značilk razvrščanje strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2020-11-03 11:17:47 2020-11-03 11:17:52 2022-09-02 04:01:20 0000-00-00 00:00:00 2020 0 0 Str. 1-9 Vol. 174 Mar. 2021 0000-00-00 PostprintKoncna Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 2022-11-03 628.8:534(045) 0003-682X 10.1016/j.apacoust.2020.107736 35370243 24982016 RAZ_Potocnik_Primoz_2020.pdf RAZ_Potocnik_Primoz_2020.pdf 1 6DB44FF7829931A30E2E94920943F82A 1e0a0afb8709bd597b8e7acf01ac15d3ee9fa4c1aee9ce3c79a39095620097c3 ff9050bf-a1ba-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=136945 https://www.sciencedirect.com/science/article/pii/S0003682X20308409?via%3Dihub 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=136944 Fakulteta za strojništvo 0 0 0