Some Results on the Random Wear Coefficient of the Archard Model

2012 ◽  
Vol 79 (5) ◽  
Author(s):  
Fabio Antonio Dorini ◽  
Rubens Sampaio

The most used model for predicting wear is the linear wear law proposed by Archard. A common generalization of Archard’s wear law is based on the assumption that the wear rate at any point on the contact surface is proportional to the local contact pressure and the relative sliding velocity. This work focuses on a stochastic modeling of the wear process to take into account the experimental uncertainties in the identification process of the contact-state dependent wear coefficient. The description of the dispersion of the wear coefficient is described by a probability density function, which is performed using the maximum entropy principle using only the information available. Closed-form results for the probability density function of the wear depth for several situations that commonly occur in practice are provided.

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Fabio Antonio Dorini ◽  
Giuseppe Pintaude ◽  
Rubens Sampaio

A very useful model for predicting abrasive wear is the linear wear law based on the Rabinowicz's equation. This equation assumes that the removed volume of the abraded material is inversely proportional to its hardness. This paper focuses on the stochastic modeling of the abrasive wear process, taking into account the experimental uncertainties in the identification process of the worn material hardness. The description of hardness is performed by means of the maximum entropy principle (MEP) using only the information available. Propagation of the uncertainties from the data to the volume of wear produced is analyzed. Moreover, comparisons and discussions with other probabilistic models for worn material hardness usually proposed in the literature are done.


2002 ◽  
Vol 14 (12) ◽  
pp. 2847-2855 ◽  
Author(s):  
Simone Fiori

This article investigates the behavior of a single-input, single-unit neuron model of the Bell-Sejnowski class, which learn through the maximum-entropy principle, in order to understand its probability density function matching ability.


Author(s):  
Yissam Lakhdar ◽  
El Hassan Sbai

In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal smoothing parameter of OVKE based on the maximum entropy principle (MEP). The robustness and performance of the proposed approach are demonstrated by examples of online estimation of real and simulated data distributions.


Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1559
Author(s):  
Nives Brajčić Kurbaša ◽  
Blaž Gotovac ◽  
Vedrana Kozulić ◽  
Hrvoje Gotovac

Estimation of the probability density function from the statistical power moments presents a challenging nonlinear numerical problem posed by unbalanced nonlinearities, numerical instability and a lack of convergence, especially for larger numbers of moments. Despite many numerical improvements over the past two decades, the classical moment problem of maximum entropy (MaxEnt) is still a very demanding numerical and statistical task. Among others, it was presented how Fup basis functions with compact support can significantly improve the convergence properties of the mentioned nonlinear algorithm, but still, there is a lot of obstacles to an efficient pdf solution in different applied examples. Therefore, besides the mentioned classical nonlinear Algorithm 1, in this paper, we present a linear approximation of the MaxEnt moment problem as Algorithm 2 using exponential Fup basis functions. Algorithm 2 solves the linear problem, satisfying only the proposed moments, using an optimal exponential tension parameter that maximizes Shannon entropy. Algorithm 2 is very efficient for larger numbers of moments and especially for skewed pdfs. Since both Algorithms have pros and cons, a hybrid strategy is proposed to combine their best approximation properties.


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