Degradation Prediction Using Regression Analysis
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Solution Overview
Problem
Current methods for predicting infrastructure structure degradation, such as those involving physical models or Markov transition probabilities, are limited in their ability to accurately predict degradation over time due to simplicity, inability to consider new factors, and lack of finer granularity, often requiring manual intervention and infrequent inspections.
Innovation Solution
A degradation prediction apparatus and method that uses a data generation unit to simulate degradation progression, a prediction model generation unit to create models for predicting degradation indices at specific times, and a degradation index prediction unit to forecast future degradation, allowing for predictions at intervals shorter than traditional periodic diagnoses without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If physical models with simple factors are used for degradation prediction, then the model construction is straightforward, but the prediction accuracy for structures with complex degradation factors is insufficient
Solution Approach 1:
The patent transforms the degradation prediction approach by changing from simple physical parameters to complex statistical parameters. It uses multiple regression analysis with numerous parameters including service environment information (traffic volume, temperature, humidity), structural characteristics, and inspection results to accurately predict degradation at fine time intervals, resolving the contradiction between model simplicity and prediction accuracy.
2Reliability
If Markov transition probability with pre-determined complex factors is used, then degradation can be predicted every year, but new factors cannot be considered and the model lacks flexibility
Solution Approach 1:
The patent makes the prediction model dynamic by allowing flexible selection and addition of parameters based on specific structures and new factors. The multiple regression analysis framework enables the model to adapt to different degradation patterns and incorporate new service environment information without being constrained by pre-determined factors, thus achieving both reliability and adaptability.
3Loss of time
If regression analysis on periodic inspection data is used, then degradation can be evaluated at set intervals, but prediction at finer time granularity than the inspection cycle is not possible
Solution Approach 1:
The patent performs preliminary action by using regression analysis to establish degradation trends from periodic inspection data, then extrapolates these trends to predict degradation at any time point within the inspection cycle. This allows prediction at fine time intervals (e.g., monthly or weekly) without requiring actual inspections at those intervals, thus reducing inspection frequency while maintaining prediction precision.
4Ease of operation
If manual intervention is required for degradation prediction between inspections, then predictions can be made based on experience and past data, but the process is time-consuming and not fully automated
Solution Approach 1:
The patent implements self-service by creating an automated prediction system that performs degradation assessment without manual intervention. The multiple regression analysis model automatically processes inspection data, service environment information, and structural characteristics to generate predictions, eliminating the need for manual experience-based judgments and significantly improving prediction efficiency while maintaining consistency.
Data Source
AI summary
In order to supplement results of diagnosis of degradation of an object that has been implemented at set intervals using a degradation progression model for simulating the progression of degradation of the object, a degradation prediction apparatus 100 is provided with: a data generation unit 112 configured to generate, as supplement data, diagnosis results that would be obtained if the degradation diagnosis were performed at an interval shorter than the set interval; a prediction model generation unit 113 configured, using the supplement data, to generate a prediction model for predicting a degradation index indicating a degradation state of the object at a specific point in time; and a degradation index prediction unit 114 configured to predict the degradation index of the object based on the prediction model.