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An improvement to the methods for estimating the statistical dependencies ofthe parameters of radom load state
Klemenc, Jernej (Author), Fajdiga, Matija (Author)

URLURL - Presentation file, Visit http://www.sciencedirect.com/science/journal/0142-1123 New window

Abstract
One of the biggest problems in the fatigue-life analysis of structures is predicting the parameters of the structure loading spectra under real operating conditions. If the loading spectrum is obtained from a load time series using a two-parametric rainflow counting method, then it is possible topresent the distribution of the corresponding load cycles in the two-dimensional space of the loading-spectrum parametersČ the amplitude and the mean of the load cycles. It is beneficial for the prediction of the load cycles if the distribution of load cycles is described by a continuous probability density function. We found in our previous studies that the different shapes of the multi-component probability density functions of load cycles can be modelled by a mixture of Gaussian functions if the parameters ofthe mixture are estimated using a maximum-likelihood method. One of the mainproblems connected with such an approach is the estimation of the number of components of the load-cycle distribution. This is usually done by a researcher, so the estimation is to a large extent subjective. In this paper, we will describe two methods for estimating the parameters of the mixture of Gaussian functions that almost eliminate the subjective influence of a researcher. The applicability of the two methods will be presented using examples of real load cases.

Language:English
Keywords:loading spectrum, rainflow method, probability density function, mixture of Gaussian functions
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2004
Number of pages:str. 141-154
Numbering:Vol. 26, no. 2
UDC:519.2
ISSN on article:0142-1123
COBISS.SI-ID:6796059 Link is opened in a new window
Views:379
Downloads:162
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Record is a part of a journal

Title:International journal of fatigue
Shortened title:Int. j. fatigue
Publisher:Elsevier Ltd.
ISSN:0142-1123
COBISS.SI-ID:25643520 New window

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