Vehicle Data Channel Ratio Analysis for Defect Detection
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Solution Overview
Problem
There is a need for a method to easily and quickly identify defective vehicles within a networked vehicle fleet, and a device and computer program to execute this method.
Innovation Solution
A method that determines data channel actual values for networked vehicles, calculates vehicle-specific ratio values, compares them to expected values, and classifies vehicles as defective based on deviation values, allowing for the identification of defective vehicles within subgroups with common properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify defective vehicles, then the identification process becomes complex and time-consuming, but using data channel ratio analysis enables quick and easy identification
Solution Approach 1:
The patent transforms the complex problem of defective vehicle identification into a simple parameter comparison task. By converting multiple data channels into ratio values and comparing these ratios against expected values, the system achieves rapid identification without complex analysis. The key parameter transformation is from raw data channel volumes to normalized ratio metrics, which simplifies the detection logic.
Solution Approach 2:
The patent replaces complex mechanical or manual inspection systems with an automated data analysis system. Instead of physical checks or manual reviews of vehicle data, the system uses automated comparison of data channel ratios against expected values, substituting a sophisticated data processing approach for simpler traditional methods.
2Measurement precision
If all vehicles are analyzed individually for defects, then accurate identification is achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent segments the vehicle fleet into subgroups based on common properties (vehicle type, production date, software version, etc.). This segmentation allows the system to analyze groups of vehicles simultaneously rather than individually, maintaining accuracy by comparing each vehicle's data channel ratios against the expected values characteristic of its specific subgroup.
Solution Approach 2:
The patent performs preliminary analysis by establishing expected data channel ratio values for each vehicle subgroup before actual defect detection. These pre-calculated expected values serve as reference benchmarks, allowing rapid comparison and identification of deviations without performing complex real-time analysis on each vehicle.
3Reliability
If data from multiple data channels is collected and analyzed, then defective vehicles can be accurately identified, but the data processing complexity increases
Solution Approach 1:
The patent extracts the essential diagnostic information by focusing on the ratio relationship between data channels rather than analyzing each data channel independently. This extraction of the ratio metric simplifies the data processing while maintaining reliability, as the ratio relationship captures the characteristic communication patterns of defective vehicles more effectively than raw data volumes.
Data Source
AI summary
A method determines at least one defective vehicle that is a subset of a specified main set of vehicles and the vehicles are divided into vehicle types. The method includes determining data channel actual values which represent a respective quantity of transmitted data in a first data channel or a second data channel for at least one vehicle. The method also includes determining a vehicle-specific ratio value based on a ratio of a data channel actual value of the first data channel to a data channel actual value of the second data channel for the at least one vehicle. The method further includes determining a deviation value for the at least one vehicle, wherein each deviation value represents a deviation of the ratio value from a specified expected value for the vehicle type, and classifying whether the at least one vehicle is defective on the basis of the deviation value.