20.500.12556/RUL-106541 Semi-supervised vibration-based classification and condition monitoring of compressors Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors. condition monitoring reciprocating compressors classification semi-supervised neural networks extreme learning machines spremljanje stanja batni kompresorji razvrščanje delno-nadzorovano nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2019-03-04 14:50:50 2019-03-04 14:50:53 2022-08-19 03:46:22 0000-00-00 00:00:00 2017 0 0 Str. 51-65 Vol. 93 Sep. 2017 0000-00-00 PostprintKoncna Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 2019-02-16 519.7:004.032.26:007(045) 0888-3270 10.1016/j.ymssp.2017.01.048 15296539 169243 RAZ_Potocnik_Primoz_i2017.pdf RAZ_Potocnik_Primoz_i2017.pdf 1 0399335A62D12EAE9C1D19B998EB2A82 2d3e827877209c3048b3f47801bcf1aea6c45f7d9e242ac3e3333fe5fdfcfe2e 5abf2b34-a1b6-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=117340 http://www.sciencedirect.com/science/article/pii/S088832701730047X 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=117339 Fakulteta za strojništvo 0 0 0