Remote Vehicle Diagnostics for Verifying Fleet Repair Quality
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Solution Overview
Problem
Current systems for evaluating the repair of remote asset fleets, such as vehicle systems, are limited by the capability of onboard software, which may not account for new diagnostic information or techniques, potentially leading to insufficient maintenance or repair.
Innovation Solution
A system and method that involve collecting vehicle data from a remote asset fleet, integrating it into a database of diagnostic data at a remote diagnostic center, and using this data to identify diagnostic codes and determine necessary maintenance operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle system software is used to evaluate maintenance or repair, then the evaluation process is automated and efficient, but the software capability is limited and may not account for new diagnostic information or techniques
Solution Approach 1:
A remote diagnostic center acts as an intermediary between the vehicle system and the final maintenance evaluation. The center receives diagnostic data from the vehicle system, performs comprehensive analysis using updated diagnostic techniques and information, and provides maintenance recommendations that overcome the limitations of onboard software while maintaining automated evaluation efficiency.
Solution Approach 2:
The evaluation process transitions from a single-dimension onboard software check to a multi-dimensional approach involving remote diagnostic analysis, historical data comparison, and expert evaluation. This adds temporal and spatial dimensions to the diagnostic process, enabling more comprehensive assessment beyond what onboard software can achieve.
2Loss of time
If onboard software determines sufficient maintenance, then the vehicle system can quickly return to operation, but the maintenance may not be sufficiently complete due to software limitations
Solution Approach 1:
The remote diagnostic center performs preliminary analysis of diagnostic data before the vehicle returns to operation. By evaluating maintenance completeness in advance and providing recommendations, the system ensures proper maintenance is completed while minimizing downtime, as issues are identified and addressed before the vehicle is returned to service.
Solution Approach 2:
The system implements a feedback loop where maintenance results are evaluated by the remote diagnostic center, and recommendations are provided back to the service center. This feedback mechanism ensures that maintenance quality is verified and improved upon, preventing premature return of improperly maintained vehicles while maintaining efficient turnaround times.
3Measurement precision
If comprehensive diagnostic analysis is performed at a remote center, then maintenance quality improves, but the complexity of the diagnostic system increases
Solution Approach 1:
The diagnostic system is segmented into two parts: onboard diagnostic data collection performed by the vehicle system itself, and comprehensive analysis performed by the remote diagnostic center. This segmentation allows the vehicle to maintain simple onboard functionality while the remote center handles the complex analytical work using aggregated data from multiple vehicles and updated diagnostic databases.
Data Source
AI summary
A system may include a processor to receive vehicle data from a vehicle system at a first location and communicate the data to a second location. The processor may integrate the data into a database of diagnostic data at the second location and identify a diagnostic code that indicates an expected operation of the vehicle system or an unexpected operation. The processor may determine a maintenance operation based on the diagnostic code. A method may include receiving vehicle data from a vehicle system at a first location and communicating the vehicle data to a second location. The method may include integrating the vehicle data into a database of first diagnostic data and identifying a diagnostic code that indicates whether the vehicle data represents an expected operation of the vehicle system or an unexpected operation of the vehicle system. The method may include determining a maintenance operation based on the code.