Gaussian mixture model based classification revisited : application to the bearing fault classification

Panić, Branislav (avtor), Klemenc, Jernej (avtor), Nagode, Marko (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: Str. 215-226
• Številčenje: Vol. 66, iss. 4
• PID: 20.500.12556/RUL-116199
• UDK: 621.82(045)
• ISSN pri članku: 0039-2480
• DOI: 10.5545/sv-jme.2020.6563
• COBISS.SI-ID: 17169179
• Datum objave v RUL: 22.05.2020

PANIĆ, Branislav, KLEMENC, Jernej in NAGODE, Marko, 2020, Gaussian mixture model based classification revisited : application to the bearing fault classification. Strojniški vestnik [na spletu]. 2020. Vol. 66, no. 4, p. 215–226. [Dostopano 4 maj 2025]. DOI 10.5545/sv-jme.2020.6563. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=116199

Gradivo je del revije

• Naslov: Strojniški vestnik
• Skrajšan naslov: Stroj. vestn.
• Založnik: Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
• ISSN: 0039-2480
• COBISS.SI-ID: 762116

Sekundarni jezik

• Jezik: Slovenski jezik
• Naslov: Preučevanje Gaussovih mešanih modelov za potrebe klasifikacije: raziskava na primeru klasifikacije napak v ležajih
• Ključne besede: Gaussov mešan model, klasifikacija, ocena napak ležajev, ocena parametrov, uspešnost klasifikacijske metod

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: 1000-18-0510