Corrosion Loss Prediction Using Latent Variables
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Solution Overview
Problem
Current methods for predicting corrosion loss of metal materials in atmospheric corrosive environments lack accuracy, particularly for long-term predictions, due to complex correlations between corrosion loss, corrosion rate, and environmental parameters, and fail to effectively account for nonlinear relationships and multicollinearity among environmental factors.
Innovation Solution
A method that calculates the similarity degree between environmental parameters in stored corrosion loss data and prediction request points, dimensionally reduces these parameters to latent variables, and uses these latent variables to construct a prediction expression for accurate long-term corrosion loss prediction, separately predicting initial corrosion loss and corrosion rate attenuation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional empirical expressions or multiple regression techniques are used to predict corrosion loss, then the prediction process is simple, but the prediction accuracy is insufficient especially for long-term predictions
Solution Approach 1:
The patent transforms the prediction approach by changing parameters from direct environmental factors to similarity degrees and latent variables. The system calculates similarity degrees between prediction targets and historical data based on environmental parameters, then uses these similarity degrees as weights in dimensional reduction to create latent variables that capture essential corrosion patterns, significantly improving prediction accuracy
Solution Approach 2:
The patent introduces similarity degree as an intermediary between environmental parameters and corrosion loss prediction. By calculating how similar current environmental conditions are to historical conditions, the system uses this similarity measure as a weighting factor in dimensional reduction, enabling more accurate predictions without directly modeling complex corrosion mechanisms
2Measurement precision
If environmental parameters are directly used in prediction formulas, then the model is easy to construct, but it cannot capture nonlinear relationships and multicollinearity among parameters
Solution Approach 1:
The patent applies dimensional reduction to transform multiple correlated environmental parameters into a smaller set of latent variables that capture the essential variation in the data. This dimensional transformation resolves multicollinearity issues while preserving nonlinear relationships, achieving accurate predictions with a simplified parameter set
Solution Approach 2:
The system changes the parameter representation from raw environmental measurements to similarity-based latent variables. By calculating similarity degrees and using them in dimensional reduction, the patent transforms the parameter space to one where nonlinear relationships and correlations are naturally captured without requiring complex explicit models
3Duration of action of moving object
If prediction is based on available historical data only, then the method is reliable for past data, but it cannot effectively predict long-term corrosion beyond the data period
Solution Approach 1:
The patent performs preliminary dimensional reduction to construct latent variables that capture the essential patterns of corrosion behavior from historical data. By pre-processing the data to extract these underlying patterns and relationships, the system creates a robust model framework that can be applied to long-term predictions even beyond the original data period
Solution Approach 2:
The system transforms the prediction capability by changing from direct interpolation of historical data to using similarity-based latent variable models. This parameter transformation allows the system to generalize from historical patterns to make accurate long-term predictions by finding similar historical conditions and applying their corrosion patterns to new time periods
Data Source
AI summary
A method of predicting a corrosion loss of a metal material, the method including: inputting a prediction request point including a use period of the metal material for which a corrosion loss is desired to be predicted and second environmental parameters indicating a use environment of the metal material in the use period; calculating a similarity degree between first environmental parameters in corrosion loss data and the second environmental parameters in the prediction request point; dimensionally reducing the first environmental parameters in the corrosion loss data to a latent variable taking the similarity degree into consideration; and predicting a corrosion loss of the metal material for the prediction request point based on a prediction expression constructed using the latent variable and the similarity degree.