20.500.12556/RUL-116199 Gaussian mixture model based classification revisited application to the bearing fault classification Preučevanje Gaussovih mešanih modelov za potrebe klasifikacije: raziskava na primeru klasifikacije napak v ležajih Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Gaussian mixture models classification bearing fault estimation parameter estimation performance of classification methods Gaussov mešan model klasifikacija ocena napak ležajev ocena parametrov uspešnost klasifikacijske metod true false true Angleški jezik Slovenski jezik Neznano 2020-05-22 08:50:04 2020-05-22 08:50:08 2022-08-28 03:47:37 0000-00-00 00:00:00 2020 0 0 Str. 215-226 iss. 4 Vol. 66 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.82(045) 0039-2480 10.5545/sv-jme.2020.6563 17169179 762116 RAZ_Panic_Branislav_2020.pdf RAZ_Panic_Branislav_2020.pdf 1 ED7275C14F3F82CB2C4E814D9F40D188 31e24e8976e0b7e43ccedde25ec5d909ec2c687f963d4ea62b086c3d4fb5ee64 d5ece9e3-a1b8-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=129616 https://www.sv-jme.eu/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/ 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=129615 Fakulteta za strojništvo Strojniški vestnik 0 0 0