Tire Abnormality Detection via Load-Based Vehicle Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
It is challenging to detect tire abnormalities early in vehicles operating in diverse environments, often resulting in significant tire degradation by the time issues are identified.
Innovation Solution
A tire abnormality management system that uses tire sensors to detect pressure and temperature, groups vehicles by similar tire loads, and identifies abnormal tires based on statistical deviations from reference ranges, enabling rapid detection of tire issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tire pressure and temperature are simply measured and compared with past data, then tire abnormality can be detected, but detection is delayed until the abnormality is already significant
Solution Approach 1:
The patent changes the detection parameters from simple absolute pressure and temperature values to normalized values that account for tire load conditions. By calculating normalized tire pressure (actual pressure divided by reference pressure at standard load) and normalized temperature, the system can detect abnormalities early regardless of varying operational loads, improving both detection accuracy and timeliness
Solution Approach 2:
The patent replaces simple threshold-based detection with a statistical analysis system that uses standard deviation calculations. Instead of comparing against fixed thresholds, the system calculates the standard deviation of normalized tire parameters across a fleet of vehicles and identifies abnormalities when individual vehicles deviate significantly from the statistical norm, enabling earlier and more accurate detection
2Quantity of substance
If tire information is collected from all vehicles regardless of operating conditions, then comprehensive data is available, but it becomes difficult to determine abnormal states due to environmental variations
Solution Approach 1:
The patent segments the fleet data by tire load conditions (light load, medium load, heavy load) and performs separate statistical analyses for each segment. This allows the system to account for environmental variations and operational differences, improving the reliability of abnormality determination by comparing vehicles under similar operating conditions rather than mixing all data together
Solution Approach 2:
The patent applies different analysis methods and reference ranges based on local operating conditions. By calculating separate standard deviations and reference ranges for each tire load segment, the system tailors the abnormality detection criteria to match the specific operating context, thereby improving determination reliability while maintaining comprehensive data collection
Data Source
AI summary
A tire abnormality management system assigns work to each of a plurality of vehicles and manages an abnormal state of a tire mounted on each of the vehicles that perform the assigned work. The tire abnormality management system includes: a tire sensor configured to detect a tire pressure and/or a tire temperature of each of the vehicles; a grouping processing unit configured to group vehicles, in which a tire load of the work assigned to each of the vehicles is within a predetermined range, into a vehicle group; and a tire abnormal vehicle identifying unit configured to identify, among the grouped vehicles, a vehicle with an abnormal tire based on the tire pressure or the tire temperature detected by the tire sensor.