Gaussian mixture model based classification revisited : application to the bearing fault classification
Panić, Branislav (Author), Klemenc, Jernej (Author), Nagode, Marko (Author)

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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.

Keywords:Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:str. 215-226
Numbering:Vol. 66, iss. 4
ISSN on article:0039-2480
DOI:10.5545/sv-jme.2020.6563 Link is opened in a new window
COBISS.SI-ID:17169179 Link is opened in a new window
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Record is a part of a journal

Title:Strojniški vestnik
Shortened title:Stroj. vestn.
Publisher:Zveza strojnih inženirjev in tehnikov Slovenije [et al.], = Association of Mechanical Engineers and Technicians of Slovenia [et al.]
COBISS.SI-ID:762116 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:1000-18-0510

Secondary language

Title:Preučevanje Gaussovih mešanih modelov za potrebe klasifikacije: raziskava na primeru klasifikacije napak v ležajih
Keywords:Gaussov mešan model, klasifikacija, ocena napak ležajev, ocena parametrov, uspešnost klasifikacijske metod

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