Scan Tool Automates DTC Diagnosis via Database Intermediary
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Solution Overview
Problem
The complexity of modern vehicle electrical systems and the variety of makes and models pose challenges for aftermarket scan tools to accurately access, process, and interpret diagnostic data from vehicle electronic control units (ECUs) and associated electrical devices, often resulting in difficulties in identifying the root cause of defects.
Innovation Solution
A diagnostic scan tool that establishes communication with a vehicle ECU, retrieves and analyzes vehicle identification number (VIN) and diagnostic trouble codes (DTCs), uses a database to identify the most likely defect solution by comparing retrieved freeze frame data with stored data, and provides a display for diagnostic information, enabling autonomous identification of the highest priority DTC and defect solution without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a scan tool is used to access diagnostic data from vehicle ECUs, then diagnostic information can be retrieved, but the complexity of processing and interpreting data from multiple makes and models increases
Solution Approach 1:
The patent uses a database as an intermediary layer between the scan tool and vehicle ECUs. The database stores pre-configured communication protocols, data formats, and diagnostic procedures for multiple vehicle makes and models. The scan tool automatically selects and applies the appropriate database configuration based on vehicle identification, eliminating the need for complex real-time protocol negotiation and data interpretation logic in the tool itself.
Solution Approach 2:
The patent performs preliminary actions by pre-storing all necessary diagnostic parameters, communication protocols, and interpretation rules in the database before actual diagnostic operations. The system pre-identifies the vehicle type and retrieves the corresponding configuration data in advance, so that the actual diagnostic data retrieval and interpretation process becomes straightforward and automated without complex real-time processing.
2Measurement precision
If manual analysis of diagnostic trouble codes is performed, then diagnostic accuracy can be achieved, but user effort and time consumption increase
Solution Approach 1:
The patent implements self-service by enabling the scan tool to automatically retrieve diagnostic trouble codes, query the database for corresponding interpretations, and present diagnostic conclusions without requiring manual analysis by the user. The system autonomously matches DTCs with stored diagnostic information, identifies potential causes, and provides repair guidance, making the diagnostic process self-executing while maintaining accuracy through the comprehensive database.
Solution Approach 2:
The patent incorporates feedback mechanisms where the scan tool automatically compares retrieved diagnostic data with expected values from the database, identifies deviations or anomalies, and uses this feedback to guide further diagnostic steps or directly present the diagnosed issue. This automated feedback loop maintains diagnostic accuracy while eliminating manual analysis efforts.
3Reliability
If comprehensive diagnostic data is collected from all vehicle systems, then complete defect identification is possible, but data processing complexity and time increase
Solution Approach 1:
The patent extracts and stores only the essential and commonly occurring diagnostic parameters, trouble codes, and their interpretations in the database. Rather than processing all possible diagnostic data from every vehicle system, the system focuses on extracting and analyzing the most relevant DTCs and associated parameters needed for accurate defect identification, thereby reducing processing time while maintaining reliability.
Solution Approach 2:
The patent segments the diagnostic process into distinct phases: automatic vehicle identification, selective DTC retrieval based on identified vehicle type, targeted query of specific subsystems, and focused analysis of relevant parameters. This segmentation allows the system to collect comprehensive data only where necessary, avoiding unnecessary data collection and processing across all vehicle systems, thus reducing time loss while maintaining complete defect identification capability.
Data Source
AI summary
A diagnostic scan tool is provided including a connect/configure module for establishing a communication link between the scan tool and a vehicle electronic control unit (ECU). A vehicle specification module operates to identify a vehicle under test in response to receipt of a vehicle identification number (VIN). A trouble code module receives digital trouble codes (DTCs) from the ECU. A freeze frame data module retrieves freeze frame data from the ECU, the retrieved freeze frame data being functionally associated with a highest priority DTC. A database lists possible vehicle defect solutions, indexed to the VIN and the DTCs. A digital signal processor is operative to derive the highest priority DTC from analysis of the retrieved freeze frame data. The digital signal processor further being operative to regulate selection of a most likely vehicle defect solution associated with the VIN and the highest priority DTC.