Wheel Service Data Processing for Tyre Wear Forecasts
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Solution Overview
Problem
Existing wheel service systems are limited to merely collecting and storing information from different measuring tools without utilizing this data for subsequent processes to provide derived quantities or signals, such as alarms, forecasts, or maintenance suggestions.
Innovation Solution
A system and method that utilizes a database to collect and process tyre and vehicle measurement parameters, including a knowledge dataset with reference data and processing criteria, to generate derived data like tyre wear indices, maintenance forecasts, and alerts, using a processor to analyze and store this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a database is used to collect and store information from different measuring tools, then information collection capability is improved, but the system remains limited to merely storing data without generating derived insights
Solution Approach 1:
The system performs preliminary actions by collecting and storing measurement parameters in advance, then uses this stored data to generate derived information such as tyre wear indices and maintenance forecasts. The database serves as a preliminary repository that enables subsequent analytical processing to extract valuable insights that would otherwise be lost.
Solution Approach 2:
The database acts as an intermediary between the measuring tools and the analytical processing system. It collects raw measurement data from various tools and makes it available for generating derived information, bridging the gap between data collection and insight generation.
2Quantity of substance
If measurement data is collected from multiple tools, then data completeness is improved, but the complexity of processing and utilizing this data increases
Solution Approach 1:
The database is designed with universal functionality to handle multiple types of measurement parameters from different tools (tyre pressure, temperature, wear depth, etc.). This multi-functional database structure simplifies processing by providing a unified interface for storing and retrieving diverse data types, reducing the overall system complexity despite data completeness improvements.
Solution Approach 2:
The system transforms raw measurement parameters into derived parameters (such as converting pressure and temperature readings into tyre condition assessments). This parameter transformation approach simplifies complexity by presenting processed, meaningful information rather than requiring users to manually analyze multiple raw parameters.
3Device complexity
If only historical storage is performed, then system simplicity is maintained, but the ability to provide predictive maintenance insights is lost
Solution Approach 1:
The system implements feedback by continuously collecting measurement data, comparing it against established criteria, and generating derived information that feeds back into maintenance decision-making. This feedback loop enables predictive maintenance insights while maintaining relative system simplicity through automated processing rules and criteria-based analysis.
Solution Approach 2:
The system performs preliminary analytical processing on collected data to generate predictive insights before maintenance is actually needed. By calculating tyre wear indices and generating maintenance forecasts in advance, the system provides predictive capabilities without requiring complex real-time analysis infrastructure.
Data Source
AI summary
A system (1) for the management of data pertaining to a wheel service includes: a database (2) including, for each tyre of a plurality of tyres, a tyre identification code; at least one tyre measurement parameter representing a physical quantity linked to the tyre or to the use thereof, and a related time mark, which indicates the time the tyre measurement parameter was captured; a knowledge dataset (3) containing reference data; a processor (4) having access to the database (2) and to the knowledge dataset (3) and programmed to process the at least one tyre measurement parameter and the reference data and to generate derived data.