Vehicle Message Deviation Analysis for Defective Fleet Detection
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Solution Overview
Problem
Existing methods for determining defective vehicles are not efficient and quick, lacking the ability to identify vehicles with common defects easily and accurately.
Innovation Solution
A method involving the calculation of expected and actual message frequencies for vehicles, determining deviation values, and identifying defective subgroups based on these deviations, utilizing an apparatus and computer program to facilitate this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used to determine defective vehicles, then the process can be carried out, but it is not efficient and quick enough
Solution Approach 1:
The patent segments the vehicle fleet into different vehicle types and further into subgroups based on common properties (software version, production date, control unit combinations). This segmentation allows parallel processing of different groups and enables targeted analysis of defective subgroups, significantly improving determination efficiency and reducing the time required to identify defective vehicles across the entire fleet.
Solution Approach 2:
The system pre-calculates expected message frequencies for each vehicle type and establishes baseline statistics before actual defect detection. By preparing reference data, vehicle type classifications, and message frequency expectations in advance, the system enables rapid comparison against actual message counts, eliminating the need for complex real-time analysis and thus improving productivity while reducing detection time.
2Measurement precision
If comprehensive analysis of all vehicles is performed, then accurate defect identification is achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent divides the complex task of analyzing all vehicles into manageable segments by classifying vehicles into types and subgroups based on common properties. This segmentation maintains measurement precision by ensuring each subgroup is analyzed against appropriate benchmarks, while simultaneously reducing process complexity through hierarchical organization and focused analysis of only relevant vehicle groups.
Solution Approach 2:
The system applies different analysis criteria and expected message frequencies to different vehicle types and subgroups based on their specific characteristics. By tailoring the detection parameters to each local group's properties (software version, production date, control units), the system achieves high accuracy for each subgroup without requiring a single complex universal analysis method for all vehicles.
3Loss of information
If detailed properties of each vehicle are analyzed, then defective subgroups with common properties are identified, but the data processing complexity increases
Solution Approach 1:
The patent segments vehicle data into structured categories (vehicle type, software version, production date, control unit combinations) and processes each category separately. This segmentation preserves complete defect information by maintaining detailed property records for each vehicle while reducing processing complexity through organized, category-based analysis rather than handling all vehicle properties as a single complex dataset.
Solution Approach 2:
The system uses a universal framework for processing vehicle properties that can handle multiple data types (software versions, production dates, control unit IDs) through a common classification and comparison mechanism. This universal approach maintains complete information about each vehicle's properties while simplifying processing by applying consistent methods across different data categories, avoiding the need for separate complex processing routines for each property type.
Data Source
AI summary
A method determines defective vehicles, wherein the defective vehicles are a subset of vehicles and the vehicles are divided into a plurality of vehicle types. Then method includes providing an expected value of a number of predefined messages to be sent for each vehicle type, and determining an actual value of a number of predefined messages sent for each vehicle. The method also includes determining a deviation value for each vehicle, wherein each deviation value is representative of a difference between the actual value for the vehicle and the expected value for a corresponding vehicle type. The method further includes determining a defective subgroup of defective vehicles depending on the deviation values, wherein the actual value for each vehicle of the defective subgroup is different from the expected value for each corresponding vehicle type.