Information Processing for Torque Limiter Damage Analysis
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Solution Overview
Problem
Existing information processing devices fail to capture data characteristics beyond vehicle speed, limiting the extraction of features that can be used to calculate the magnitude of damage in torque limiters.
Innovation Solution
An information processing device that extracts data using a search process involving clustering, time windows, and relative frequency distributions to reduce data amount while maintaining analysis accuracy, by calculating an index value indicating torque limiter damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed by extracting only vehicle speed data, then data volume is reduced, but data characteristics beyond vehicle speed are lost
Solution Approach 1:
The patent segments the original data into multiple features (vehicle speed, acceleration, torque limiter slippage amount, slippage duration, etc.) and processes each feature independently through clustering and time window extraction. This allows selective preservation of important characteristics while reducing overall data volume.
Solution Approach 2:
The patent changes parameters by introducing multiple extraction conditions based on different features (vehicle speed thresholds, acceleration thresholds, slippage amount thresholds, slippage duration thresholds) instead of relying on a single vehicle speed parameter. This enables comprehensive capture of data characteristics across multiple dimensions.
2Measurement precision
If all original data is used for analysis, then analysis accuracy is maintained, but data processing time increases
Solution Approach 1:
The patent extracts representative data segments from the original data using clustering and time window techniques. By identifying and extracting only the most relevant portions of data that capture essential characteristics, it reduces processing time while maintaining analysis accuracy.
Solution Approach 2:
The patent uses partial action by extracting a subset of data (through time windows and clustering) that is sufficient for accurate analysis without processing the entire original dataset. The extraction is designed to capture essential characteristics without being excessive.
3Device complexity
If data extraction focuses on single feature, then extraction process is simple, but comprehensive characteristics cannot be captured
Solution Approach 1:
The patent applies a universal multi-functional extraction framework that handles multiple features (vehicle speed, acceleration, torque limiter slippage amount, slippage duration) using the same clustering and time window methodology. This unified approach captures comprehensive characteristics without significantly increasing process complexity.
Data Source
AI summary
The processing device of the information processing device 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 clipping data of a partial period of the original data; a third step of clipping 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 process of repeatedly executing the trial from the second step to the fifth step by changing the setting of the plurality of time windows.