Reliability Analysis and Design Considering Disjointed Active Failure Regions

2015 ◽  
Author(s):  
Pingfeng Wang ◽  
Xiaolong Cui ◽  
Zequn Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Failure Modes ◽  
Kriging Model ◽  
Engineering Systems ◽  
Random Input ◽  
Performance Function ◽  
Analysis And Design ◽  
Practical Engineering

Failure of practical engineering systems could be induced by several correlated failure modes, and consequently reliability analysis are conducted with multiple disjointed failure regions in the system random input space. Problems with disjointed failure regions create a great challenge for existing reliability analysis approaches due to the discontinuity of the system performance function between these regions. This paper presents a new enhanced Monte Carlo simulation (EMCS) approach for reliability analysis and design considering disjointed failure regions. The ordinary Kriging method is adopted to construct surrogate model for the performance function so that Monte Carlo simulation (MCS) can be used to estimate the reliability. A maximum failure potential based sampling scheme is developed to iteratively search failure samples and update the Kriging model. Two case studies are used to demonstrate the efficacy of the proposed methodology.

Reliability Analysis and Design Considering Disjointed Active Failure Regions

2015 ◽  
Author(s):  
Pingfeng Wang ◽  
Xiaolong Cui
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Failure Modes ◽  
Kriging Model ◽  
Engineering Systems ◽  
Random Input ◽  
Performance Function ◽  
Analysis And Design ◽  
Practical Engineering

Failure of practical engineering systems could be induced by several correlated failure modes, and consequently reliability analysis are conducted with multiple disjointed failure regions in the system random input space. Problems with disjointed failure regions create a great challenge for existing reliability analysis approaches due to the discontinuity of the system performance function between these regions. This paper presents a new enhanced Monte Carlo simulation (EMCS) approach for reliability analysis and design considering disjointed failure regions. The ordinary Kriging method is adopted to construct surrogate model for the performance function so that Monte Carlo simulation (MCS) can be used to estimate the reliability. A maximum failure potential based sampling scheme is developed to iteratively search failure samples and update the Kriging model. Two case studies are used to demonstrate the efficacy of the proposed methodology.

A SEQUENTIAL AUGMENTED METAMODELING METHOD FOR ENGINEERING RELIABILITY ANALYSIS

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Qian Wang ◽  
Jun Ji
Keyword(s):  
Sample Size ◽  
Reliability Analysis ◽  
Limit State ◽  
Engineering Systems ◽  
Analysis Method ◽  
Model Accuracy ◽  
Performance Function ◽  
Analysis And Design ◽  
Optimal Sample ◽  
Practical Engineering

Metamodeling methods provide useful tools to replace expensive numerical simulations in engineering reliability analysis and design optimization. The radial basis functions (RBFs) and augmented RBFs can be used to create accurate metamodels; therefore they can be integrated with a reliability analysis method such as the Monte Carlo simulations (MCS). However the model accuracy of RBFs depends on the sample size, and the accuracy generally increases as the sample size increases. Since the optimal sample size used to create RBF metamodels is not known before the creation of the models, a sequential RBF metamodeling method was studied. In each iteration of reliability analysis, augmented RBFs were used to generate metamodels of a limit state or performance function, and the failure probability was calculated using MCS. Additional samples were generated in subsequent analysis iterations in order to improve the metamodel accuracy. Numerical examples from literature were solved, and the failure probabilities based on the RBF metamodels were found to have a good accuracy. In addition, only small numbers of iterations were required for the reliability analysis to converge. The proposed method based on sequential RBF metamodels is useful for probabilistic analysis of practical engineering systems.

scholarly journals Mechanism reliability and sensitivity analysis of landing gear under multiple failure modes

2021 ◽  
Vol 39 (1) ◽  
pp. 46-54
Author(s):  
Changcong Zhou ◽  
Mengyao Ji ◽  
Yishang Zhang ◽  
Fuchao Liu ◽  
Haodong Zhao
Keyword(s):  
Failure Probability ◽  
Failure Modes ◽  
Kriging Model ◽  
Landing Gear ◽  
Multiple Failure Modes ◽  
Analysis And Design ◽  
Extension System ◽  
Extension Mechanism ◽  
Assembly Error ◽  
Adaptive Kriging

For a certain type of aircraft landing gear retraction-extension mechanism, a multi-body dynamic simulation model is established, and the time-dependent curves of force and angle are obtained. Considering the random uncertainty of friction coefficient, assembly error, and the change of hinge wear under different retraction times, the reliability model is built including three failure modes of landing gear, i.e. blocking failure, positioning failure and accuracy failure. Based on the adaptive Kriging model, the reliability and sensitivity of retraction-extension system under the condition of single failure mode and multiple failure modes in series are analyzed, and the rule of reliability and sensitivity changing with the number of operations is given. The results show that the system failure probability of landing gear mechanism tends to decrease first and then increase when considering the given information of random factors, and the influences of random factors on the failure probability vary with the number of operations. This work provides a viable tool for the reliability analysis and design of landing gear mechanisms.

A New Reliability Analysis Method Combining Adaptive Kriging With Weight Index Monte Carlo Simulation

2018 ◽  
Vol 54 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Jiangang Ma ◽  
Ziyan Ren ◽  
Guoxin Zhao ◽  
Yanli Zhang ◽  
Chang-Seop Koh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Analysis Method ◽  
Weight Index ◽  
Adaptive Kriging
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