Vehicle Component Diagnostic System Using Weather and Usage Thresholds
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Solution Overview
Problem
Current systems fail to efficiently and accurately collect data on vehicle component states, such as tire tread depth and battery charge levels, leading to inefficiencies and safety concerns in vehicle operation.
Innovation Solution
A computer-based system that collects data from sensors and infrastructure sensors to identify degraded states of vehicle components, such as tires and batteries, and actsuates vehicles to resolve these states by assigning routes or moving them to maintenance stations based on thresholds for usage and weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data collection from vehicles and external sources is performed manually or with basic systems, then system complexity is reduced, but data collection efficiency and accuracy deteriorate
Solution Approach 1:
The patent combines multiple data sources (vehicle sensors, infrastructure sensors, weather services, maintenance records) into a unified diagnostic system that automatically collects and analyzes data from all sources simultaneously, improving efficiency without proportionally increasing complexity through integration
Solution Approach 2:
The system introduces an intermediary diagnostic platform that acts as a mediator between raw data sources and decision-making processes, automatically processing data from multiple sources and translating it into actionable maintenance insights, thereby improving data collection efficiency while managing complexity through abstraction
2Measurement precision
If vehicle component monitoring is performed with basic threshold checks, then system complexity is reduced, but measurement precision and diagnostic accuracy deteriorate
Solution Approach 1:
The system transitions from simple threshold-based monitoring to multi-parameter analysis, evaluating component health based on usage patterns, environmental conditions, weather data, and historical maintenance records simultaneously, thereby improving diagnostic precision while managing complexity through systematic parameter integration
Solution Approach 2:
The system implements continuous feedback loops where diagnostic results inform subsequent monitoring and maintenance decisions, automatically adjusting diagnostic parameters and maintenance schedules based on observed component behavior and performance data, improving accuracy through adaptive learning
3Productivity
If vehicle maintenance is performed on fixed schedules, then operational simplicity is maintained, but productivity and efficiency deteriorate due to unnecessary maintenance or missed issues
Solution Approach 1:
The system transitions from static fixed-schedule maintenance to dynamic condition-based maintenance, automatically adjusting maintenance timing and requirements based on real-time component health assessment, usage patterns, and environmental factors, thereby improving operational efficiency while managing complexity through automated adaptive scheduling
4Reliability
If comprehensive data collection from multiple sources is implemented, then diagnostic reliability improves, but loss of information and data processing burden increase
Solution Approach 1:
The system extracts and prioritizes only the most relevant diagnostic information from comprehensive data sources, filtering out redundant or low-value data while focusing on critical component health indicators, thereby improving diagnostic reliability while reducing information overload through selective extraction
Data Source
AI summary
A system includes a computer including a processor and a memory, the memory including instructions executable by the processor to identify a degraded state of a vehicle component when a component usage exceeds a first threshold and a weather datum exceeds a second threshold, and then actuate the vehicle component in response to the degraded state.