Rolling Equipment Deterioration Diagnosis by Condition-Based Segmentation
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Solution Overview
Problem
Existing methods struggle to accurately determine the presence/absence of equipment deterioration in rolling processes due to interference between equipment machines and materials being rolled, which are affected by varying rolling conditions.
Innovation Solution
A deterioration diagnosing device that classifies monitoring parameters based on rolling conditions, using ARX models to calculate representative values, and employs outlier exclusion and statistical methods like Hotelling's T2 and Shewhart control charts to determine equipment health with precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monitoring parameters are obtained by directly comparing past time-series data with present-time data, then the monitoring process is simple, but the precision of determining equipment deterioration is insufficient due to interference from rolling conditions and materials
Solution Approach 1:
The patent segments the monitoring parameters by dividing them into multiple categories based on different rolling conditions (steel grade, sheet thickness, sheet width, goal temperature). This segmentation allows comparison of parameters under similar conditions only, eliminating interference from varying rolling conditions and improving deterioration detection precision without requiring complex interference correction mechanisms
Solution Approach 2:
The patent changes the parameter organization structure by introducing category-based grouping of monitoring parameters. Instead of direct time-series comparison, parameters are reorganized into categories according to rolling conditions, and representative values are calculated for each category. This parameter transformation enables accurate deterioration detection while maintaining manageable system complexity
2Measurement precision
If monitoring parameters are classified according to rolling conditions, then the precision of deterioration determination is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The system performs self-service by automatically calculating representative values for each category of monitoring parameters and automatically comparing them against baseline data. This automation eliminates the need for manual data processing and analysis, achieving high precision deterioration determination while keeping the operational complexity low through automated algorithms
Solution Approach 2:
The patent transforms complex categorized monitoring data into simplified representative values through mathematical processing. This parameter transformation reduces the dimensionality of the data while preserving the essential information needed for deterioration detection, thereby improving precision without proportionally increasing processing complexity
3Measurement precision
If representative values are calculated from categorized monitoring parameters, then accurate deterioration detection is achieved, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial action by selecting and calculating representative values only for key monitoring parameters within each category, rather than processing all available data comprehensively. This selective approach achieves accurate deterioration detection while reducing the time and computational resources required compared to full data processing
Data Source
AI summary
A deterioration diagnosing device of a rolling equipment machine includes an input/output data obtaining unit, a model identifying unit, a monitoring parameter calculating unit, a monitoring parameter usage determining unit, a representative value calculating unit, a representative value storage unit, and a deterioration diagnosing unit. The monitoring parameter usage determining unit includes a categorized monitoring parameter acquiring function for acquiring the monitoring parameters, so as to be classified according to categories designated depending on a rolling condition. The representative value calculating unit calculates a representative value of monitoring parameters from a predetermined time period corresponding to each of the categories. The representative value storage unit accumulates, with respect to each of the categories, the representative values over a learning period designated from a start of a monitoring process. The deterioration diagnosing unit includes a categorized deterioration determining function that determines presence/absence of deterioration with respect to each of the categories.