Load spectra growth modelling and extrapolation with REBMIX

In the field of predicting structural safety and reliability the operating conditions play an essential role. Since the time and cost limitations are a significant factors in engineering it is important to predict the future operating conditions as close to the actual state as possible from small amount of available data. Because of the randomness of the environment the shape of measured load spectra can vary considerably and therefore simple distribution functions are frequently not sufficient for their modelling. Thus mixed distribution functions have to be used. In general their major weakness is the complicated calculation of unknown parameters. The scope of the paper is to investigate the load spectra growth for actual operating conditions and to investigate the modelling and extrapolation of load spectra with algorithm for mixed distribution estimation, REBMIX. The data obtained from the measurements of wheel forces and the braking moment on proving ground is used to generate load spectra.

[1]  M. Baker,et al.  On the probability density function of rainflow stress range for stationary Gaussian processes , 1992 .

[2]  C. Amzallag,et al.  Standardization of the rainflow counting method for fatigue analysis , 1994 .

[3]  Marko Nagode,et al.  On a new method for prediction of the scatter of loading spectra , 1998 .

[4]  M. Nagode,et al.  A general multi-modal probability density function suitable for the rainflow ranges of stationary random processes , 1998 .

[5]  Marko Nagode,et al.  The influence of variable operating conditions upon the general multi-modal Weibull distribution , 1999 .

[6]  Roberto Tovo A damage-based evaluation of probability density distribution for rain-flow ranges from random processes , 2000 .

[7]  Marko Nagode,et al.  An improved algorithm for parameter estimation suitable for mixed Weibull distributions , 2000 .

[8]  H. A. David,et al.  A note on the variance of a lightly trimmed mean when multiple outliers are present in the sample , 2001 .

[9]  Marko Nagode,et al.  Parametric modelling and scatter prediction of rainflow matrices , 2001 .

[10]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Matija Fajdiga,et al.  An alternative perspective on the mixture estimation problem , 2006, Reliab. Eng. Syst. Saf..

[12]  Matija Fajdiga,et al.  Predicting smoothed loading spectra using a combined multilayer perceptron neural network , 2006 .

[13]  Milan Stehlík Exact likelihood ratio scale and homogeneity testing of some loss processes , 2006 .

[14]  C. Prioul,et al.  Cold Drawn Steel Wires-Processing, Residual Stresses and Ductility-Part I: Metallography and Finite Element Analyses , 2006 .

[15]  Milan Stehlík,et al.  Homogeneity and scale testing of generalized gamma distribution , 2008, Reliab. Eng. Syst. Saf..