Vehicle Diagnostic Data Visualization System
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Solution Overview
Problem
Monitoring and tracking vehicle and engine component failures in large fleets is challenging, leading to progressive damage and increased costs due to the difficulty in diagnosing and addressing issues in a timely manner.
Innovation Solution
A data visualization system that aggregates and interprets vehicle and technician data to facilitate remote monitoring, providing a graphical user interface with configurable options for displaying vehicle operation characteristics, diagnostic data, and recommended actions to address potential root causes of faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle and engine components are monitored in large fleets, then reliability is improved, but device complexity increases
Solution Approach 1:
A centralized server acts as an intermediary between multiple vehicles and technicians. The server receives diagnostic data from vehicles, processes it, and delivers it to technicians via mobile devices. This intermediary architecture allows reliable fleet-wide monitoring without requiring complex distributed systems at each vehicle location.
Solution Approach 2:
The system creates digital copies of diagnostic data and vehicle states that can be transmitted and analyzed remotely. Instead of physically inspecting each vehicle, technicians access replicated data representations through mobile devices, enabling reliable monitoring across large fleets with minimal additional physical infrastructure.
2Loss of time
If diagnostic data is collected and analyzed, then loss of time is reduced, but loss of information increases
Solution Approach 1:
The system extracts only the most critical diagnostic data and alert information from vehicles for immediate transmission to technicians. By filtering and selecting only essential information (such as fault codes, critical parameter deviations, and alert conditions), the system reduces data transmission requirements while maintaining rapid diagnostic capability.
Solution Approach 2:
Different levels of data are provided to different users based on their needs. The server provides detailed raw data to technicians who need it for analysis, while providing summarized alert information to fleet managers. This localized data quality approach minimizes overall data transmission while ensuring each user receives appropriate information.
3Ease of operation
If remote monitoring is implemented, then ease of operation is improved, but measurement precision decreases
Solution Approach 1:
The system performs preliminary data processing, filtering, and validation at the server before presenting information to technicians. Diagnostic data is pre-analyzed to identify potential issues, and the server prepares structured presentations of the data that maintain precision while enabling easy remote access through mobile devices.
Data Source
AI summary
A method includes receiving vehicle data indicative of a least one operating characteristic of one or more vehicles and one or more fault codes from the one or more vehicles, each of the one or more fault codes having a corresponding component; aggregating the one or more fault codes based on the corresponding components of the one or more fault codes; interpreting the vehicle data and the one or more fault codes to determine a potential root cause for the one or more fault codes by comparing vehicle data of a first vehicle of the one or more vehicles to the vehicle data of the one or more vehicles; and providing a graphical user interface to a display device for depicting the one or more fault codes based on the potential root cause.