Rare Failure Event Detection via Hyperbox Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting rare failure events in complex socio-technical systems face high computational complexity and degeneration issues, leading to high variance and lack of diversity in simulation results, making it difficult to accurately determine rare failure probabilities.
Innovation Solution
A method and system that generate a system model of a socio-technical system using a model generator unit, select essential system parameters, and determine hyperboxes using sampling optimization techniques to represent confidence regions for rare events, followed by variance reduction techniques to calculate statistically significant probabilities of these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Monte Carlo simulation methods are used to estimate failure rates, then the detection capability for rare failure events is provided, but the computational complexity becomes very high
Solution Approach 1:
The parameter space is segmented into multiple hyperboxes based on essential system parameters. This segmentation allows the simulation to focus computational resources on specific regions of the parameter space that are more likely to contain rare failure events, thereby reducing overall computational complexity while maintaining detection precision.
Solution Approach 2:
The method applies different sampling strategies to different hyperboxes based on their local characteristics. Hyperboxes with higher probability of containing rare events receive more intensive sampling, while others receive less. This local quality approach optimizes the distribution of computational resources across the parameter space.
2Device complexity
If sequential Monte Carlo methods are used to reduce computational complexity, then iterative simulation optimization is performed, but the methods may degenerate after a few successive re-sampling steps
Solution Approach 1:
The method performs preliminary identification of essential system parameters and their probability distributions before conducting the main simulation. This preliminary action prepares the parameter space segmentation in advance, allowing the sequential Monte Carlo method to operate more efficiently and reducing the likelihood of degeneration during successive re-sampling steps.
Solution Approach 2:
The method dynamically adjusts sampling parameters and hyperbox boundaries during the simulation process based on observed event distributions. This parameter adaptation prevents the simulation from stagnating or degenerating by continuously optimizing the search strategy for rare failure events.
3Measurement precision
If sequential Monte Carlo methods are applied to a Markov process to enhance rare failure event detection, then the detection capability is improved, but high variance and lack of diversity occur
Solution Approach 1:
The method transitions from traditional one-dimensional sequential Monte Carlo sampling to multi-dimensional hyperbox-based sampling in the parameter space. This dimensional expansion allows the simulation to explore diverse regions of the parameter space simultaneously, reducing variance and improving the diversity of simulated outcomes while maintaining enhanced detection capability.
4Measurement precision
If traditional simulation techniques are used for socio-technical systems, then failure and accident rates can be estimated, but the computational complexity is very high
Solution Approach 1:
The method extracts and identifies essential system parameters from the complex socio-technical system model. By focusing simulation efforts on these essential parameters rather than all system parameters, the method achieves accurate failure rate estimation with significantly reduced computational complexity, thereby improving computational efficiency.
Data Source
AI summary
A method includes generating a system model representative of a socio-technical system having a plurality of system parameters. The method further includes selecting one or more essential system parameters from the plurality of system parameters. The method also includes determining a plurality of probability distributions corresponding to the one or more essential system parameters. The method further includes determining at least one hyperbox using a sampling optimization technique based on the one or more essential system parameters. The at least one hyperbox is representative of a confidence region corresponding to a rare event of the socio-technical system. The method also includes determining a probability of the rare event using a variance reduction technique based on a plurality of particles obtained from the at least one hyperbox. The probability of the rare event is representative of a performance of the socio-technical system.