A novel method for estimating the failure possibility by combining the adaptive Kriging model with the Markov chain simulation

2021 ◽  
pp. 107205
Author(s):  
Jiaqi Wang ◽  
Zhenzhou Lu ◽  
Lu Wang
Keyword(s):  
Markov Chain ◽  
Kriging Model ◽  
Novel Method ◽  
Adaptive Kriging ◽  
Failure Possibility

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.

scholarly journals Realistic Choice of Annual Matrices Contracts the Range of λS Estimates

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2252
Author(s):  
Dmitrii O. Logofet ◽  
Leonid L. Golubyatnikov ◽  
Nina G. Ulanova
Keyword(s):  
Markov Chain ◽  
Local Population ◽  
Quantitative Measure ◽  
Weather Data ◽  
Variable Environment ◽  
Annual Population ◽  
Novel Method ◽  
Stochastic Growth Rate ◽  
Realistic Choice

In matrix population modeling the multi-year monitoring of a population structure results in a set of annual population projection matrices (PPMs), which gives rise to the stochastic growth rate λS, a quantitative measure of long-term population viability. This measure is usually found in the paradigm of population growth in a variable environment. The environment is represented by the set of PPMs, and λS ensues from a long sequence of PPMs chosen at random from the given set. because the known rules of random choice, such as the iid (independent and identically distributed) matrices, are generally artificial, the challenge is to find a more realistic rule. We achieve this with the a following a Markov chain that models, in a certain sense, the real variations in the environment. We develop a novel method to construct the ruling Markov chain from long-term weather data and to simulate, in a Monte Carlo mode, the long sequences of PPMs resulting in the estimates of λS. The stochastic nature of sequences causes the estimates to vary within some range, and we compare the range obtained by the “realistic choice” from 10 PPMs for a local population of a Red-Book species to those using the iid choice. As noted in the title of this paper, this realistic choice contracts the range of λS estimates, thus improving the estimation and confirming the Red-Book status of the species.

scholarly journals Star formation law in the epoch of reionization from [C ii] and C iii] lines

2020 ◽  
Vol 495 (1) ◽  
pp. L22-L26 ◽  
Author(s):  
L Vallini ◽  
A Ferrara ◽  
A Pallottini ◽  
S Carniani ◽  
S Gallerani
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Star Formation ◽  
Markov Chain Monte Carlo ◽  
Star Formation Rate ◽  
Formation Rate ◽  
Average Density ◽  
Monte Carlo Algorithm ◽  
James Webb Space Telescope ◽  
Novel Method

ABSTRACT We present a novel method to simultaneously characterize the star formation law and the interstellar medium properties of galaxies in the epoch of reionization (EoR) through the combination of [C ii] 158 μm (and its known relation with star formation rate) and C iii] λ1909 Å emission line data. The method, based on a Markov chain Monte Carlo algorithm, allows us to determine the target galaxy average density, n, gas metallicity, Z, and ‘burstiness’ parameter, κs, quantifying deviations from the Kennicutt–Schmidt relation. As an application, we consider COS-3018 (z = 6.854), the only EoR Lyman Break Galaxy so far detected in both [C ii] and C iii]. We show that COS-3018 is a moderate starburst (κs ≈ 3), with $Z \approx 0.4 \, \mathrm{Z}_{\odot }$, and $n \approx 500\, {\rm cm^{-3}}$. Our method will be optimally applied to joint ALMA and James Webb Space Telescope targets.

scholarly journals Fast Compression of MCMC Output

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1017
Author(s):  
Nicolas Chopin ◽  
Gabriel Ducrocq
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Equality Constraints ◽  
Survey Sampling ◽  
Monte Carlo Algorithm ◽  
Control Variates ◽  
Novel Method

We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube method (an approach that originates from survey sampling). The main advantage of cube thinning is that its complexity does not depend on the size of the compressed sample. This compares favourably to previous methods, such as Stein thinning, the complexity of which is quadratic in that quantity.

scholarly journals A Concise Transformation Combined With Adaptive Kriging Model for Efficiently Estimating Regional Sensitivity on Failure Probability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135457-135471
Author(s):  
Jingyu Lei ◽  
Zhenzhou Lu ◽  
Yingshi Hu ◽  
Beixi Jia
Keyword(s):  
Failure Probability ◽  
Kriging Model ◽  
Adaptive Kriging

scholarly journals A Fuzzy Approach for the Kinematic Reliability Assessment of Robotic Manipulators

Robotica ◽  
2021 ◽  
pp. 1-15
Author(s):  
Fabian A. Lara-Molina ◽  
Didier Dumur
Keyword(s):  
Fuzzy Theory ◽  
Probabilistic Approach ◽  
Reliability Assessment ◽  
Robotic Manipulators ◽  
Fuzzy Reliability ◽  
End Effector ◽  
Positioning Errors ◽  
Novel Method ◽  
Joint Clearances ◽  
Failure Possibility

SUMMARY This paper aims at developing a novel method to assess the kinematic reliability of robotic manipulators based on the fuzzy theory. The kinematic reliability quantifies the probability of obtaining positioning errors within acceptable limits. For this purpose, the fuzzy reliability evaluates the effect of the joint clearances on the end-effector position to compute a failure possibility index. As an alternative to the conventional methods reported in the literature, this failure possibility index conveys a novel assessment of the kinematic performance. The numerical results are compared with the well-known probabilistic approach based on the Monte Carlo simulation.

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