The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions

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Izvoz citacije: ABNT
KLEMENC, Jernej ;FAJDIGA, Matija .
The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 47, n.10, p. 593-604, july 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/>. Date accessed: 05 jun. 2020. 
doi:http://dx.doi.org/.
Klemenc, J., & Fajdiga, M.
(2001).
The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions.
Strojniški vestnik - Journal of Mechanical Engineering, 47(10), 593-604.
doi:http://dx.doi.org/
@article{.,
	author = {Jernej  Klemenc and Matija  Fajdiga},
	title = {The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {47},
	number = {10},
	year = {2001},
	keywords = {Gaussian functions; probability density; maximum likelihood methods; EM algorithm; },
	abstract = {To evaluate the fatigue damage of a dynamically loaded structure, a time history of the structure load should be acquired first. This can be done by means of experiments or simulations. When the time history of the loads is known, it should be transformed into a form that is suitable for the prediction of the fatigue damage. This fatigue damage of the structure depends heavily on the load cycles that are included in the load time history. Load cycles are extracted from the load time history with different counting methods. A rainflow counting method is widely used in the automotive industry. The rainflow counting method results in a matrix of the relative frequencies of the load cycles, which are included in the load time history. With an approximation of the rainflow matrix by a continuous probability density function, random fluctuations of the relative frequencies are reduced and an extrapolation of the probability of the load cycles that were not actually recorded is made possible. In our paper a method of modelling the rainflow matrices by means of a mixture of Gaussian functions will be presented. Unknown parameters of the normal mixture will be estimated with a maximum-likelihood method. The effectiveness of this method will be presented and discussed with an example of the load time histories of a real structure.},
	issn = {0039-2480},	pages = {593-604},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/}
}
Klemenc, J.,Fajdiga, M.
2001 July 47. The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 47:10
%A Klemenc, Jernej 
%A Fajdiga, Matija 
%D 2001
%T The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions
%B 2001
%9 Gaussian functions; probability density; maximum likelihood methods; EM algorithm; 
%! The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions
%K Gaussian functions; probability density; maximum likelihood methods; EM algorithm; 
%X To evaluate the fatigue damage of a dynamically loaded structure, a time history of the structure load should be acquired first. This can be done by means of experiments or simulations. When the time history of the loads is known, it should be transformed into a form that is suitable for the prediction of the fatigue damage. This fatigue damage of the structure depends heavily on the load cycles that are included in the load time history. Load cycles are extracted from the load time history with different counting methods. A rainflow counting method is widely used in the automotive industry. The rainflow counting method results in a matrix of the relative frequencies of the load cycles, which are included in the load time history. With an approximation of the rainflow matrix by a continuous probability density function, random fluctuations of the relative frequencies are reduced and an extrapolation of the probability of the load cycles that were not actually recorded is made possible. In our paper a method of modelling the rainflow matrices by means of a mixture of Gaussian functions will be presented. Unknown parameters of the normal mixture will be estimated with a maximum-likelihood method. The effectiveness of this method will be presented and discussed with an example of the load time histories of a real structure.
%U https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/
%0 Journal Article
%R 
%& 593
%P 12
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 47
%N 10
%@ 0039-2480
%8 2017-07-07
%7 2017-07-07
Klemenc, Jernej, & Matija  Fajdiga.
"The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions." Strojniški vestnik - Journal of Mechanical Engineering [Online], 47.10 (2001): 593-604. Web.  05 Jun. 2020
TY  - JOUR
AU  - Klemenc, Jernej 
AU  - Fajdiga, Matija 
PY  - 2001
TI  - The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - Gaussian functions; probability density; maximum likelihood methods; EM algorithm; 
N2  - To evaluate the fatigue damage of a dynamically loaded structure, a time history of the structure load should be acquired first. This can be done by means of experiments or simulations. When the time history of the loads is known, it should be transformed into a form that is suitable for the prediction of the fatigue damage. This fatigue damage of the structure depends heavily on the load cycles that are included in the load time history. Load cycles are extracted from the load time history with different counting methods. A rainflow counting method is widely used in the automotive industry. The rainflow counting method results in a matrix of the relative frequencies of the load cycles, which are included in the load time history. With an approximation of the rainflow matrix by a continuous probability density function, random fluctuations of the relative frequencies are reduced and an extrapolation of the probability of the load cycles that were not actually recorded is made possible. In our paper a method of modelling the rainflow matrices by means of a mixture of Gaussian functions will be presented. Unknown parameters of the normal mixture will be estimated with a maximum-likelihood method. The effectiveness of this method will be presented and discussed with an example of the load time histories of a real structure.
UR  - https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/
@article{{}{.},
	author = {Klemenc, J., Fajdiga, M.},
	title = {The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {47},
	number = {10},
	year = {2001},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/}
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TY  - JOUR
AU  - Klemenc, Jernej 
AU  - Fajdiga, Matija 
PY  - 2017/07/07
TI  - The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 47, No 10 (2001): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - Gaussian functions, probability density, maximum likelihood methods, EM algorithm, 
N2  - To evaluate the fatigue damage of a dynamically loaded structure, a time history of the structure load should be acquired first. This can be done by means of experiments or simulations. When the time history of the loads is known, it should be transformed into a form that is suitable for the prediction of the fatigue damage. This fatigue damage of the structure depends heavily on the load cycles that are included in the load time history. Load cycles are extracted from the load time history with different counting methods. A rainflow counting method is widely used in the automotive industry. The rainflow counting method results in a matrix of the relative frequencies of the load cycles, which are included in the load time history. With an approximation of the rainflow matrix by a continuous probability density function, random fluctuations of the relative frequencies are reduced and an extrapolation of the probability of the load cycles that were not actually recorded is made possible. In our paper a method of modelling the rainflow matrices by means of a mixture of Gaussian functions will be presented. Unknown parameters of the normal mixture will be estimated with a maximum-likelihood method. The effectiveness of this method will be presented and discussed with an example of the load time histories of a real structure.
UR  - https://www.sv-jme.eu/sl/article/the-modelling-of-rainflow-matrices-with-a-mixture-of-gaussian-functions/
Klemenc, Jernej, AND Fajdiga, Matija.
"The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 47 Number 10 (07 July 2017)

Avtorji

Inštitucije

  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia
  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 47(2001)10, 593-604

To evaluate the fatigue damage of a dynamically loaded structure, a time history of the structure load should be acquired first. This can be done by means of experiments or simulations. When the time history of the loads is known, it should be transformed into a form that is suitable for the prediction of the fatigue damage. This fatigue damage of the structure depends heavily on the load cycles that are included in the load time history. Load cycles are extracted from the load time history with different counting methods. A rainflow counting method is widely used in the automotive industry. The rainflow counting method results in a matrix of the relative frequencies of the load cycles, which are included in the load time history. With an approximation of the rainflow matrix by a continuous probability density function, random fluctuations of the relative frequencies are reduced and an extrapolation of the probability of the load cycles that were not actually recorded is made possible. In our paper a method of modelling the rainflow matrices by means of a mixture of Gaussian functions will be presented. Unknown parameters of the normal mixture will be estimated with a maximum-likelihood method. The effectiveness of this method will be presented and discussed with an example of the load time histories of a real structure.

Gaussian functions; probability density; maximum likelihood methods; EM algorithm;