Automotive Diagnostic Process Using Prior Experience Database
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Solution Overview
Problem
The conventional process for diagnosing and repairing vehicles using diagnostic trouble codes (DTCs) is cumbersome and expensive, as it requires extensive analysis and access to multiple resources, often leading to delays and uncertainties due to the need to correlate DTCs with underlying problems and appropriate repair procedures.
Innovation Solution
A method that processes vehicle diagnostic data by mapping it to possible fixes using a prior experience database, prioritizing solutions based on matched diagnostic codes, and linking the most likely fix to a repair procedure, thereby streamlining the identification and execution of necessary repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic processes are used, then comprehensive analysis of vehicle conditions is achieved, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing diagnostic data from multiple vehicles in a database before the actual diagnosis is needed. Historical diagnostic information, repair outcomes, and vehicle data are prepared in advance, allowing the system to quickly match current diagnostic codes with previous cases without requiring real-time analysis of all possible vehicle configurations.
Solution Approach 2:
The invention introduces an intermediary database system that mediates between the diagnostic codes and the repair procedures. Instead of requiring the mechanic to directly analyze complex vehicle systems and manually search through extensive documentation, the database serves as an intermediary that processes diagnostic information, matches it with historical data, and returns relevant repair procedures, thereby reducing the time and effort required for diagnosis.
2Reliability
If extensive analysis of mechanical, electrical, and software systems is performed, then accurate diagnosis is achieved, but the process requires considerable effort and expense
Solution Approach 1:
The system extracts only the essential diagnostic information needed for accurate diagnosis from the complex web of mechanical, electrical, and software systems. By focusing on diagnostic codes and their direct associations with repair procedures, the system separates the critical diagnostic elements from the unnecessary complexity of analyzing every possible system interaction, thereby maintaining diagnosis reliability while reducing analysis complexity.
Solution Approach 2:
The invention creates a simplified copy of the complex diagnostic and repair information in the form of a structured database. This database contains condensed versions of vehicle data, diagnostic codes, and repair procedures, allowing the system to work with simplified representations rather than the full complexity of actual vehicle systems, thus reducing the effort and expense required for analysis while maintaining diagnostic accuracy.
3Loss of information
If multiple online resources and manuals are accessed, then comprehensive repair information is obtained, but the process introduces further delays and uncertainties
Solution Approach 1:
The system merges multiple separate information sources including diagnostic databases, repair manuals, and historical repair records into a single integrated database system. This consolidation allows the mechanic to access all necessary information (diagnostic codes, vehicle data, repair procedures, and historical outcomes) in one location rather than having to navigate multiple separate resources, thereby maintaining information completeness while significantly reducing the time required to access and evaluate repair information.
Data Source
AI summary
A method of processing vehicle diagnostic data is provided for identifying likely vehicle fix(s) associated with a diagnostic data, and identifying a repair procedure(s) for correcting the likely fix(s). The process receiving vehicle diagnostic data from a vehicle onboard computer at a remote diagnostic database, the database being arranged to map vehicle diagnostic data to possible vehicle fix(s). The possible vehicle fix(s) are prioritized in accordance with ranked matches of the received diagnostic data to combinations of diagnostic data stored in a prior experience database. The prior experience database having an identified fix associated with each stored combination of diagnostic data. The fix associated with the highest ranked combination of diagnostic data is identified as the most likely fix. The most likely fix is mapped to a vehicle repair database, the most likely fix being directly mapped to an associated repair procedure for repairing the most likely fix.