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Fatigue life prediction of wire rope based on grey particle filter method under small sample condition

Zhao Dan, Liu Yu-Xin, Ren Xun-Tao, Gao Jing-Zi, Liu Shao-Gang, Dong Li-Qiang, Cheng Ming-Shen

Eksploatacja i Niezawodność

2021

Vol. 23, no. 3

454--467

The fatigue life prediction of wire ropes has two main characteristics: a large test sample size and uncertain factors. In this paper, based on the small number of wire rope fatigue life data, the grey particle filter method has been used to realize the fatigue life prediction of wire rope under different load conditions. First, the GOM(1,1) model is constructed and the reliability life data of wire rope is predicted under small sample size. Then, P-S-N curve of the dangerous part is determined by combining the equivalent alternating stress of the dangerous part of the wire rope during the fatigue test. Subsequently, the particle filter method is used to modify P-S-N curve. Finally, the fatigue life prediction model of wire rope is obtained based on fatigue damage accumulation, which realized the fatigue life prediction under different load conditions, and the results were compared with that from the test. The results show that the proposed method is effective and has high accuracy in wire rope fatigue life prediction under single, combined loading conditions and small sample size.

Approach to solving mining machine selection problem by using grey theory

Milisavljevic V., Martinetti A., Cvjetic A.

Mining – Informatics, Automation and Electrical Engineering

2018

R. 56, nr 3

59--64

The selection of a mining machine is a multiple-attribute problem that involves the consideration of numerous parameters of various origins. A common task in the mining industry is to select the best machine among several alternatives, which are frequently described both with numerical variables as well as linguistic variables. Numerical variables are mostly related to the technical characteristics of the machines, which are available in detail in most cases. On the other hand, some equally important parameters such as price, reliability, support for service and spare parts, operating cost, etc., are not available at the required level for various reasons; hence, these can be considered uncertain information. For this reason, such information is described with linguistic variables. This paper presents research related to overcoming this problem by using grey theory for selecting a proper mining machine. Grey theory is a well-known method used for multiple-attribute selection problems that involves a system in which parts of the necessary information are known and parts are unknown.

An univariate DEMR modelling on repair effects

Guo R.

Journal of Polish Safety and Reliability Association

2007

Vol. 1

157--165

Repairable system analysis is in nature an evaluation of repair effects. Recent tendency in reliability engineering literature was estimating system repair effects or linking repair to certain covariate to extract repair impacts by imposing repair regimes during system reliability analysis. In this paper, we develop a differential equation motivated regression (abbreviated as DEMR) model with a random fuzzy error term based on the axiomatic framework of self-dual fuzzy credibility measure theory proposed by Liu [5] and grey differential equation models. The fuzzy variable indexes the random fuzzy error term will be used to facilitate the evaluation of repair effects. We further propose a parameter estimation approach for the fuzzy variable (repair effect) under the maximum entropy principle.