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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.

Estimation of the ischemic brain temperature with the particle filter method

Nunes Felipe S., Orlande Helcio R. B., Nowak Andrzej J.

Computer Assisted Methods in Engineering and Science

2019

Vol. 26, no. 1

5--19

In this work, a two-dimensional model was developed to analyze the transient temperature distribution in the head of a newborn human, during local cooling promoted by the flow of cold water through a cap. The inverse problem dealt with the sequential estimation of the internal temperature of the head, by performing non-invasive transient temperature measurements. A state estimation problem was solved with the sampling importance resampling (SIR) algorithm of the particle filter method. Uncertainties in the evolution and observation models were assumed as additive, Gaussian, uncorrelated and with zero means. The uncertainties for the evolution model were obtained from the Monte Carlo simulations, based on the uncertainties of the model parameters. The head temperature was accurately predicted with the particle filter method. Such a technique might be applied in the future to monitor the brain temperature of newborns and control the local cooling treatment of neonatal hypoxic-ischemic encephalopathy.