Fleet Vehicle Defect Detection by Message Deviation Analysis
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Solution Overview
Problem
Existing methods are inefficient in quickly and easily identifying defective vehicles within a networked fleet, particularly when defects affect a subset of vehicles sharing common characteristics.
Innovation Solution
A method involving determining expected and actual message transmission values for vehicles, calculating deviation values, and identifying defective subgroups based on these values, along with characteristics like vehicle type, software version, and production date, using a device and computer program to facilitate rapid identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify defective vehicles, then identification can be performed, but the process is inefficient and time-consuming
Solution Approach 1:
The system pre-calculates and stores expected message transmission values for each vehicle type before actual identification occurs. This preliminary preparation enables rapid comparison with actual values during the identification process, eliminating the need for complex real-time calculations and significantly reducing identification time.
Solution Approach 2:
The patent replaces traditional manual or complex mechanical inspection methods with an automated electronic system that compares actual message transmission data against pre-stored expected values. This substitution of electronic data processing for manual inspection dramatically improves identification efficiency and reduces time loss.
2Measurement precision
If comprehensive vehicle analysis is performed to ensure accurate defect identification, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts and compares only the specific characteristic of message transmission counts against pre-stored expected values for each vehicle type. By focusing on this single extracted parameter rather than analyzing all vehicle characteristics simultaneously, the system achieves accurate defect identification while maintaining simplicity and avoiding unnecessary complexity.
3Measurement precision
If detailed vehicle characteristics are analyzed to identify defective subgroups, then identification precision improves, but processing complexity increases
Solution Approach 1:
The system segments the vehicle fleet into distinct vehicle types and establishes separate expected message transmission values for each type. This segmentation allows for precise identification of defective subgroups by comparing actual values against type-specific expectations, improving precision while keeping processing manageable through organized categorization.
Data Source
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AI summary
The invention relates to a method for determining defective vehicles, the defective vehicles being a subset of vehicles (2) and the vehicles (2) being divided into a plurality of vehicle types, said method comprising: - providing (S1) an expected value of a number of predefined messages to be sent for each vehicle type, - determining (S2) an actual value of sent predefined messages for each vehicle (2), - determining (S3) a deviation value for each vehicle (2), each deviation value being representative of a difference between the actual value and the expected value, - determining (S4) a defective subgroup of defective vehicles depending on the deviation values, the actual value of the defective subgroup being different from the expected value. The invention also relates to an apparatus, a computer programme, and a computer-readable storage medium.