Dynamic reliability models for multiple dependent competing degradation processes

Conventional reliability models of planetary gear systems are mainly static. In this paper, dynamic reliability models and random lifetime models of planetary gear systems are developed with dynamic working mechanism considered. The load parameters, the geometric parameters, and the material parameters are taken as the inputs of the reliability models and the random lifetime models. Moreover, failure dependence and dynamic random load redistributions are taken into account in the models. Monte Carlo simulations are carried out to validate the proposed models. The results show that the randomness of the load distribution is obvious in the system working process. Failure dependence has significant influences on system reliability. Moreover, the dispersion of external load has great impacts on the reliability, lifetime distribution, and redundancy of planetary gear systems.

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Dynamic Reliability Analysis Method of Degraded Mechanical Components Based on Process Probability Density Function of Stress

Mathematical Problems in Engineering ◽

10.1155/2014/109892 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Peng Gao ◽

Liyang Xie

Keyword(s):

Probability Density Function ◽

Probability Density ◽

Density Function ◽

Solar Array ◽

Dynamic Reliability ◽

Reliability Models ◽

Practical Engineering ◽

Mechanical Components ◽

Reliability Computation ◽

Probability Density Function Pdf

It is necessary to develop dynamic reliability models when considering strength degradation of mechanical components. Instant probability density function (IPDF) of stress and process probability density function (PPDF) of stress, which are obtained via different statistical methods, are defined, respectively. In practical engineering, the probability density function (PDF) for the usage of mechanical components is mostly PPDF, such as the PDF acquired via the rain flow counting method. For the convenience of application, IPDF is always approximated by PPDF when using the existing dynamic reliability models. However, it may cause errors in the reliability calculation due to the approximation of IPDF by PPDF. Therefore, dynamic reliability models directly based on PPDF of stress are developed in this paper. Furthermore, the proposed models can be used for reliability assessment in the case of small amount of stress process samples by employing the fuzzy set theory. In addition, the mechanical components in solar array of satellites are chosen as representative examples to illustrate the proposed models. The results show that errors are caused because of the approximation of IPDF by PPDF and the proposed models are accurate in the reliability computation.

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Prediction of Corroded Pipeline Performance Based on Dynamic Reliability Models

Procedia CIRP ◽

10.1016/j.procir.2019.01.093 ◽

2019 ◽

Vol 80 ◽

pp. 518-523 ◽

Cited By ~ 2

Author(s):

Reza Aulia ◽

Henry Tan ◽

Srinivas Sriramula

Keyword(s):

Dynamic Reliability ◽

Reliability Models

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Dynamic reliability models of mechanical load-sharing parallel systems considering strength degradation of components

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214560000 ◽

2014 ◽

Vol 229 (13) ◽

pp. 2484-2495 ◽

Cited By ~ 2

Author(s):

Peng Gao ◽

Liyang Xie

Keyword(s):

Failure Rate ◽

Mechanical Load ◽

Parallel Systems ◽

Load Sharing ◽

Strength Degradation ◽

Random Load ◽

Dynamic Reliability ◽

Reliability Models ◽

Load Redistribution ◽

Mechanical Components

Conventional reliability analysis of load-sharing parallel systems is mainly based on failure rate of components, in which failure dependence of components and load redistribution are also characterized by specified failure rates. However, the failure rate of mechanical components always varies with time, which is difficult to measure. Therefore, in this paper, quantitative dynamic reliability models of mechanical load-sharing parallel systems are developed in terms of stress parameters and strength parameters rather than failure rate of components, which consider the degradation mechanism of mechanical components. The proposed models take into account the strength degradation path dependence (SDPD) of a component, the strength degradation process dependence between different components in a system, and the random load redistribution. In addition, Monte Carlo simulation is carried out to verify the proposed models. The results show that SDPD and the load-sharing effect have considerable influences on dynamic reliability of mechanical load-sharing parallel systems.

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Adjoint Sensitivity Analysis of Dynamic Reliability Models Based on Markov Chains—I: Theory

Nuclear Science and Engineering ◽

10.13182/nse08-a2742 ◽

2008 ◽

Vol 158 (2) ◽

pp. 97-113 ◽

Cited By ~ 3

Author(s):

Dan G. Cacuci ◽

Mihaela Ionescu-Bujor

Keyword(s):

Sensitivity Analysis ◽

Markov Chains ◽

Adjoint Sensitivity Analysis ◽

Adjoint Sensitivity ◽

Dynamic Reliability ◽

Reliability Models

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Dynamic Reliability Models for Software Using Time-Dependent Covariates

Technometrics ◽

10.1198/004017005000000292 ◽

2006 ◽

Vol 48 (1) ◽

pp. 1-10 ◽

Cited By ~ 16

Author(s):

Bonnie K Ray ◽

Zhaohui Liu ◽

Nalini Ravishanker

Keyword(s):

Time Dependent ◽

Dynamic Reliability ◽

Reliability Models ◽

Time Dependent Covariates

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DYNAMIC RELIABILITY MEASURES AND LIFE DISTRIBUTION MODELS FOR MULTISTATE SYSTEMS

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539395000083 ◽

1995 ◽

Vol 02 (01) ◽

pp. 79-102 ◽

Cited By ~ 8

Author(s):

KAI YANG ◽

JIANAN XUE

Keyword(s):

Markov Process ◽

Probability Models ◽

Reliability Measures ◽

Distribution Models ◽

Dynamic Reliability ◽

Reliability Models ◽

Reliability Parameters ◽

Multistate System ◽

Binary State ◽

Semi Markov Process

This paper generalizes the dynamic binary state reliability parameters R(t), F(t), λ(t) and MTBF to corresponding dynamic multistate reliability parameter vectors R(t), F(t), λ(t) and M. Then, probability models for system lifetime used on binary state reliability models, such as exponential, Weibull, and other distributions are generalized for multistate models. Continuous time Markov process and Semi-Markov process are used to model the lifetime distribution for multistate system. Multistate reliability measures, such as R(t), F(t), λ(t), M are derived for those multistate reliability models.

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