Application of a Particle Filter-Based Subset Simulation Method to the Reliability Assessment of a Marine Structure

2012 ◽  
Author(s):  
Zakoua Guédé ◽  
Alexandru Tantar ◽  
Emilia Tantar ◽  
Pierre Del Moral
Keyword(s):  
Particle Filter ◽  
Failure Probability ◽  
Branching Process ◽  
Simulation Method ◽  
Classical Particle ◽  
Filter Method ◽  
Subset Simulation ◽  
Simulation Techniques ◽  
Marine Structure ◽  
Best Parameter

The present study aims at investigating advanced subset simulation techniques, which are based on the theory of particle filter, for the assessment of the failure probability of a marine structure under extreme loading conditions. Three approaches are considered, namely the classical particle filter method, the subset simulation with a branching process and one using the minimum values of the samples as levels. They are, first, intensively applied on a simple example for which a known analytical solution is available, in order to investigate their parameter settings. Then, they are applied, with good performance, using their respective best parameter settings, to the assessment of failure probability of a FPSO subjected to extreme roll motion.

Method for Estimating Failure Probability of Mechanical Structures Using Number Theoretical Net (NT-net) Simulation

2009 ◽  
Vol 628-629 ◽  
pp. 239-244
Author(s):  
Z.J. Wen ◽  
De Shun Liu ◽  
Shu Yi Yang
Keyword(s):  
Failure Probability ◽  
Simulation Method ◽  
Computational Effort ◽  
Side Impact ◽  
Computational Accuracy ◽  
Simulation Techniques ◽  
Net Method ◽  
Good Lattice ◽  
Good Lattice Point ◽  
Point Set

According to poor computational accuracy at small to median sample sizes of Monte Carlo ( MC ) simulation techniques in estimating the probability failure of mechanical structures, the number theoretical net ( NT-net ) simulation method is proposed to reduce computing effort. Several key concepts, such as good point set, good-lattice point ( glp ), discrepancy and NT-net method, are defined. The sampling stategy is improved by introducing NT-net that can provide better convergent rate over MC. The new method is used to estimate failure probability of the side impact bar on the car door. Results indicate the computational effort needed by NT-net for the same accuracy is about 1/12 of that needed by the MC-based method, and the obtained results are more stable.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Koji Tamaribuchi ◽  
Stephen Wu
Keyword(s):  
Particle Filter ◽  
Early Warning ◽  
Seismic Intensity ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Estimation Accuracy ◽  
Filter Method ◽  
Source Estimation ◽  
Single Station ◽  
Seismic Networks

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error <0.25  s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.

Corrosion failure probability analysis of buried gas pipelines based on subset simulation

2019 ◽  
Vol 57 ◽  
pp. 25-33 ◽  
Author(s):  
Aihua Liu ◽  
Ke Chen ◽  
Xiaofei Huang ◽  
Jieyun Chen ◽  
Jianfeng Zhou ◽  
...  
Keyword(s):  
Failure Probability ◽  
Probability Analysis ◽  
Gas Pipelines ◽  
Subset Simulation ◽  
Corrosion Failure

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

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