Adaboost Surrogate Model for Sealing Structure Reliability Evaluation
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Solution Overview
Problem
Traditional methods for evaluating the reliability of sealing structures in multi-failure modes are inefficient due to complex finite element modeling, long calculation times, and difficulty in simulating coupling effects, making it challenging to assess the reliability of complex structures under varied failure conditions.
Innovation Solution
The method employs an Adaboost algorithm for iterative classification training using a binary classification algorithm and an important sampling method to calculate failure probabilities, reducing calculation time and improving accuracy by focusing on critical samples and expanding variance for efficient simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional finite element calculation combined with Monte Carlo simulation is used to evaluate reliability of sealing structure, then failure probability can be calculated directly, but calculation time is long and device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the Adaboost classification model offline using historical failure data and finite element results. This preprocessing step creates a ready-to-use classification system that can quickly evaluate new samples without requiring real-time finite element analysis, thus reducing calculation time while maintaining accuracy.
Solution Approach 2:
The patent creates a simplified copy of the complex finite element analysis by training an Adaboost classification model that replicates the failure prediction capability. Instead of running full finite element simulations for each evaluation, the system uses the trained classification model as a lightweight surrogate that captures the essential failure patterns.
2Reliability
If traditional finite element modeling is used for sealing structure reliability evaluation, then failure process can be simulated, but modeling complexity increases and application range is narrow
Solution Approach 1:
The patent replaces the complex mechanical finite element analysis system with an information-processing classification system. The Adaboost algorithm processes input parameters (pressure, temperature, material properties) through computational classification rather than physical simulation, substituting mechanical computation with algorithmic decision-making.
Solution Approach 2:
The patent changes the evaluation approach from simulating physical failure processes to classifying based on parameter patterns. By transforming the problem from spatial-temporal finite element simulation to parameter-based classification, the system achieves broader applicability across different sealing structures without requiring complex geometric modeling.
3Measurement precision
If Monte Carlo simulation is used to calculate failure probability, then reliability can be evaluated, but simulation time is long and efficiency is low
Solution Approach 1:
The patent creates a simplified surrogate model (Adaboost classification model) that copies the essential failure prediction capability of Monte Carlo simulation. This surrogate model can be trained once and then rapidly applied to numerous cases, replacing the need for repeated time-consuming Monte Carlo simulations while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary training of the classification model offline using a dataset generated from finite element analysis and Monte Carlo simulation. This preprocessing creates a ready-to-use model that encapsulates failure probability relationships, enabling rapid evaluation without repeating the full simulation process for each new case.
4Reliability
If traditional methods are used to simulate coupling effect of multi-failure mode, then comprehensive reliability assessment can be achieved, but calculation complexity and time increase significantly
Solution Approach 1:
The patent segments the complex multi-failure mode problem into separate classification tasks for each failure mode. The Adaboost model can be trained independently for different failure modes (e.g., extrusion, torsion, compression) and then combined through probabilistic integration, breaking down the intractable coupled problem into manageable pieces that can be solved efficiently.
Solution Approach 2:
The patent transforms the complex coupled failure analysis into a series of parameter-based classification problems. By changing from physical coupling simulation to parameter space classification, the system can handle multiple failure modes by processing their respective parameter sets through the classification model, significantly reducing computational burden.
Data Source
AI summary
A method for evaluating the reliability of a sealing structure in a multi-failure mode based on an Adaboost algorithm. The Adaboost algorithm is adopted to carry out a classification iterative training on the seal ring failure related data of a small sample until a classification error of set classifier meets a precision requirement; then the failure probability of the sealing structure is calculated under the fluctuation condition of related parameters by adopting the important sampling method, and further the reliability of the sealing structure is evaluated in the multi-failure mode. The present invention solves the problems of long time consumption and complex calculation process of reliability evaluation in multi-failure mode of the complex structure.