Vehicle Data Processing for Lock-Up Clutch Damage Estimation
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Solution Overview
Problem
Existing information processing apparatuses fail to extract data that captures features of the entirety of original data, including quantities other than vehicle speed, leading to inefficiencies in data analysis.
Innovation Solution
An information processing apparatus that extracts data by calculating relative frequency distributions, performing clustering, and setting time windows to reduce data amount while maintaining accuracy, using a processing device to analyze data from vehicles to estimate damage in a lock-up clutch of a torque converter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed by extracting only vehicle speed-related data, then data amount is reduced, but analysis accuracy deteriorates because features other than vehicle speed are lost
Solution Approach 1:
The patent changes the selection criteria from single-parameter (vehicle speed only) to multi-parameter (multiple feature quantities including vehicle speed, acceleration, steering angle, brake operation, engine speed, and transmission gear). This allows comprehensive data extraction that maintains analytical accuracy while reducing data volume by 90% or more.
Solution Approach 2:
The patent performs preliminary clustering analysis to identify representative time periods before final data extraction. By pre-identifying clusters with high damage correlation based on multiple feature quantities, the system ensures that extracted data from these representative periods maintains equivalent analysis accuracy to using all original data.
2Measurement precision
If all original data is used for analysis, then analysis accuracy is maintained, but processing time increases due to large data volume
Solution Approach 1:
The patent extracts only essential data from representative time periods identified through clustering analysis. By selecting and extracting only the most relevant data portions that capture the essential characteristics of the entire dataset, the system reduces processing time while maintaining analysis accuracy equivalent to using all original data.
Solution Approach 2:
The patent uses a partial action approach by analyzing only data from representative time periods (e.g., 10-20% of total time) rather than the complete dataset. This partial analysis is sufficient to achieve equivalent accuracy while significantly reducing processing time and computational resources.
3Device complexity
If data is extracted using only vehicle speed as the criterion, then data extraction is simple, but the extracted data cannot grasp features of the entirety of the original data
Solution Approach 1:
The patent creates a universal extraction framework that handles multiple feature quantities (vehicle speed, acceleration, steering angle, brake operation, engine speed, transmission gear) through a single clustering-based approach. This multi-functional system maintains comprehensive data feature representation while managing complexity through unified processing logic.
Data Source
AI summary
The processing device of the information processing apparatus includes: a first step of calculating a relative frequency distribution of the original data; a second step of setting a plurality of time windows for cutting out data of a part of the period of the original data; a third step of cutting out data from the original data; a fourth step of calculating a relative frequency distribution in the extracted data; and a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data, and performs a search processing of repeatedly executing the trial from the second step to the fifth step by changing the setting of the plurality of time windows.