System reliability assessment method for traveler on bridge construction stage

Author(s):  
R Nie ◽  
X Shi ◽  
X Ruan
2021 ◽  
Vol 11 (21) ◽  
pp. 10258
Author(s):  
Xiaopeng Li ◽  
Fuqiu Li

A space station is a typical phased-mission system, and assessing its reliability during its configuration is an important engineering action. Traditional methods usually require extensive data to carry out a layered reliability assessment from components to the system. These methods suffer from lack of sufficient test data, and the assessment process becomes very difficult, especially in the early stage of the configuration. This paper proposes a reliability assessment method for the space station configuration mission, using multi-layer and multi-type risks. Firstly, the risk layer and the risk type for the space station configuration are defined and identified. Then, the key configuration risks are identified comprehensively, considering their occurrence likelihood and consequence severity. High load risks are identified through risk propagation feature analysis. Finally, the configuration reliability model is built and the state probabilities are computed, based on the probabilistic risk propagation assessment (PRPA) method using the assessment probability data. Two issues are addressed in this paper: (1) how to build the configuration reliability model with three layers and four types of risks in the early stage of the configuration; (2) how to quantitatively assess the configuration mission reliability using data from the existing operational database and data describing the propagation features. The proposed method could be a useful tool for the complex aerospace system reliability assessment in the early stage.


2020 ◽  
Vol 251 ◽  
pp. 119786
Author(s):  
Jun-Gang Zhou ◽  
Ling-Ling Li ◽  
Ming-Lang Tseng ◽  
Guo-Qian Lin

Author(s):  
Qinglai Dong ◽  
Weiwei Wang ◽  
Shubin Si

With the aim of solving the reliability modeling and calculation of multivariate stochastic degradation systems, two stochastic degradation models based on the bivariate Wiener process are proposed, in which two performance characteristics are composited to one variable. Two different failure modes including the defect-based failure and the duration-based failure are considered. The explicit expressions of the system reliability are derived in the cases that the performance characteristics are not composited or the performance characteristics are composited according to the linear combination of the degradation measurements. An algorithm based on the Monte Carlo simulation is proposed to simulate the degradation process, in which the performance characteristics are composited in arbitrary forms, and the correctness of the analytical results is also verified. Finally, some numerical examples are presented to illustrate the present reliability assessment method。


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hebing Luan ◽  
Jiachen Wang ◽  
Guowei Ma ◽  
Ke Zhang

Roof cutting has long been a potential hazard factor in longwall panels in some diggings in China. Meanwhile, the key strata structural reliability, which provides an assessment on the stability of overlying roof strata, may be a significant reference for support design in underground coal mines. This paper aims to investigate a practical nonprobabilistic reliability assessment method on key strata. The mechanical tests and the hollow inclusion triaxial strain tests were conducted to measure relevant mechanical parameters and in situ stress. Furthermore, against the typical failure features in Datong Diggings, China, a shear failure mechanical model of key strata is proposed. Then, an allowable-safety-factor based nonprobabilistic stability probability assessment method is given. The sensitivity of geometrical dimensions and uncertainty levels of friction angle and cohesion are further studied. It is found that thickness and span of key strata have more dominative effect on key strata’s stability compared with the other factor and the increase of uncertainty levels results in decrease of stability probability.


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