Stress–strength reliability models involving generalized gamma and Weibull distributions

On the Stress-Strength Reliability of Transmuted GEV Random Variables with Applications to Financial Assets Selection

In reliability contexts, probabilities of the type R=P(X Collapse

Key Words

• TGEV distribution
• assets selection
• extreme-value ℍ-function
• stress–strength reliability

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MESH Headings Collapse

Grants

• 304716/2023-5 CNPq
• 309674/2020-4 CNPq

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Affiliation(s)

Melquisadec Oliveira Department of Statistics, University of Brasília, Brasília 70910-900, DF, Brazil; (M.O.); (F.S.Q.); (P.N.R.)
Felipe S. Quintino Department of Statistics, University of Brasília, Brasília 70910-900, DF, Brazil; (M.O.); (F.S.Q.); (P.N.R.)
Dióscoros Aguiar Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Jataí, Jataí 75801-615, GO, Brazil;
Pushpa N. Rathie Department of Statistics, University of Brasília, Brasília 70910-900, DF, Brazil; (M.O.); (F.S.Q.); (P.N.R.)
Helton Saulo Department of Statistics, University of Brasília, Brasília 70910-900, DF, Brazil; (M.O.); (F.S.Q.); (P.N.R.)
Tiago A. da Fonseca Gama Engineering College, University of Brasília, Brasília 72444-240, DF, Brazil;
Luan Carlos de Sena Monteiro Ozelim Department of Civil and Environmental Engineering, University of Brasília, Brasília 70910-900, DF, Brazil;

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Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution

In this paper, an inference on stress-strength reliability model is introduced in case of the exponentiated generalized Marshall Olkin G family of distributions. The maximum likelihood estimator of the stress-strength reliability function is deduced. An asymptotic confidence and bootstrap confidence intervals for the stress-strength reliability function are derived. A Bayesian inference is introduced for the stress-strength reliability. A simulation is introduced to obtain the maximum likelihood and Bayesian estimates for the stress strength reliability. Real data applications are provided to show the results for the stress-strength model and compare the exponentiated generalized Marshall Olkin-G distribution with other distributions.

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

Due to the effects of noise disturbances and system resilience, the current methods for rolling bearing fault feature extraction and degradation trend estimation can hardly achieve more satisfactory results. To address the above issues, we propose a different method for fault feature extraction and degradation trend estimation. Firstly, we preset the Bayesian inference criterion to evaluate the complexity of the denoised vibration signal. When its complexity reaches a minimum, the noise disturbances are exactly removed. Secondly, we define the system resilience obtained by the Bayesian network as the intrinsic index of the system, which is used to correct the equipment degradation trend obtained by the multivariate status estimation technique. Finally, the effectiveness of the proposed method is verified by the completeness of the extracted fault features and the accuracy of the degradation trend estimation over the whole life cycle of the bearing degradation data.