PANIĆ, Branislav ;KLEMENC, Jernej ;NAGODE, Marko . Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 66, n.4, p. 215-226, april 2020. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2020.6563.
Panić, B., Klemenc, J., & Nagode, M. (2020). Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification. Strojniški vestnik - Journal of Mechanical Engineering, 66(4), 215-226. doi:http://dx.doi.org/10.5545/sv-jme.2020.6563
@article{sv-jmesv-jme.2020.6563,
author = {Branislav Panić and Jernej Klemenc and Marko Nagode},
title = {Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {66},
number = {4},
year = {2020},
keywords = {Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods},
abstract = {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.},
issn = {0039-2480}, pages = {215-226}, doi = {10.5545/sv-jme.2020.6563},
url = {https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/}
}
Panić, B.,Klemenc, J.,Nagode, M. 2020 April 66. Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 66:4
%A Panić, Branislav %A Klemenc, Jernej %A Nagode, Marko %D 2020 %T Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification %B 2020 %9 Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods %! Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification %K Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods %X 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. %U https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/ %0 Journal Article %R 10.5545/sv-jme.2020.6563 %& 215 %P 12 %J Strojniški vestnik - Journal of Mechanical Engineering %V 66 %N 4 %@ 0039-2480 %8 2020-04-17 %7 2020-04-17
Panić, Branislav, Jernej Klemenc, & Marko Nagode. "Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification." Strojniški vestnik - Journal of Mechanical Engineering [Online], 66.4 (2020): 215-226. Web. 20 Dec. 2024
TY - JOUR AU - Panić, Branislav AU - Klemenc, Jernej AU - Nagode, Marko PY - 2020 TI - Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2020.6563 KW - Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods N2 - 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. UR - https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/
@article{{sv-jme}{sv-jme.2020.6563},
author = {Panić, B., Klemenc, J., Nagode, M.},
title = {Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {66},
number = {4},
year = {2020},
doi = {10.5545/sv-jme.2020.6563},
url = {https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/}
}
TY - JOUR AU - Panić, Branislav AU - Klemenc, Jernej AU - Nagode, Marko PY - 2020/04/17 TI - Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 66, No 4 (2020): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2020.6563 KW - Gaussian mixture models, classification, bearing fault estimation, parameter estimation, performance of classification methods N2 - 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. UR - https://www.sv-jme.eu/sl/article/gaussian-mixture-model-based-classification-revisited-application-to-the-bearing-fault-classification/
Panić, Branislav, Klemenc, Jernej, AND Nagode, Marko. "Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 66 Number 4 (17 April 2020)
Strojniški vestnik - Journal of Mechanical Engineering 66(2020)4, 215-226
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.