Vehicle Diagnostic Data Aggregation and Fault Extraction
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Solution Overview
Problem
The increasing amount of diagnostic data from vehicle communication systems makes manual analysis of faults time-consuming, as relevant data needs to be manually extracted from a large quantity of available data, hindering efficient storage and analysis.
Innovation Solution
A method involving a buffer memory and an aggregation component to aggregate and filter diagnostic data, using a finite deterministic automaton to identify fault events and extract relevant messages, which are then stored with unique designators to reduce redundancy and optimize storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all diagnostic data from vehicle communication systems is stored for analysis, then the completeness of diagnostic information is improved, but the time required to analyze faults increases due to manual extraction from large quantities of data
Solution Approach 1:
The system performs preliminary actions by automatically aggregating messages and pre-filtering diagnostic data during the data collection phase. The aggregation component combines multiple raw messages into structured diagnostic records before storage, and the evaluation component pre-identifies relevant data patterns, so that when fault analysis is needed, the data is already organized and ready for quick retrieval without manual extraction
Solution Approach 2:
The patent introduces intermediary components between data collection and analysis: an aggregation component that structures raw messages into standardized formats, and an evaluation component that pre-processes data according to diagnostic rules. These intermediaries transform the raw data stream into organized diagnostic records that can be efficiently queried and analyzed, reducing the time burden on analysts while preserving complete diagnostic information
2Quantity of substance
If diagnostic data is aggregated using mapping rules to form aggregated messages, then the quantity of data to be analyzed is reduced, but the complexity of the storage system increases
Solution Approach 1:
The storage system is segmented into distinct functional components: a raw message buffer for storing incoming data, an aggregation component for processing messages according to mapping rules, an evaluation component for assessing diagnostic conditions, and a structured storage component for organized data retention. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while enabling effective data aggregation and reduction
Solution Approach 2:
The aggregation component acts as an intermediary that transforms raw messages into structured aggregated messages using predefined mapping rules. This intermediary layer handles the complexity of data transformation and normalization, allowing the storage system to maintain organized, reduced-quantity data without requiring the entire system to be complex. The intermediary absorbs the aggregation logic, keeping the storage and retrieval mechanisms relatively simple
3Measurement precision
If manual extraction of relevant diagnostic data is performed from total available data, then accurate fault analysis is achieved, but the productivity of diagnostic processes decreases
Solution Approach 1:
The diagnostic system performs self-service by automatically evaluating and filtering diagnostic data according to predefined rules and criteria. The evaluation component autonomously identifies relevant diagnostic information and extracts key fault indicators without requiring manual intervention. This self-service capability maintains accurate fault analysis by applying consistent evaluation logic while significantly improving diagnostic productivity by eliminating time-consuming manual extraction processes
Data Source
AI summary
A method for storing vehicle diagnostic data includes the steps of: reading one or more messages including vehicle diagnostic data; transmitting the read messages to a buffer memory; transmitting the read messages to an aggregation component; aggregating the read messages by use of the aggregation component to form an aggregated message; transmitting the aggregated message to the buffer memory; checking whether a predefined fault event occurred during aggregation of the messages; if a predefined fault event occurred: transmitting the predefined fault event to an extraction component, the predefined fault event including at least one predefined rule; executing the at least one predefined rule by the extraction component in order to extract the messages corresponding to the at least one predefined rule from the buffer memory; and storing the extracted messages and the fault event in a data memory, such that the extracted messages can be identified using the fault event.