Vehicle Status Evaluation Using Multi-Component Driving Behavior
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Solution Overview
Problem
Current vehicle monitoring systems provide only partial information about the status of vehicle components, failing to effectively assess the health and performance of multi-component modules (MCMs) and individual components, leading to incomplete diagnostics and potential premature failures.
Innovation Solution
A method that evaluates the status of vehicle components and MCMs by processing sensed information from driving sessions, using machine learning and big data technologies to predict future faults and provide preventive measures, and generates comprehensive Vehicle Curriculum Vitae (VCV) reports for better condition evaluation, diagnostics, and maintenance insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional sensors are used to monitor vehicle components, then some basic status information can be obtained, but the information is only partial and insufficient for comprehensive component health assessment
Solution Approach 1:
The system uses existing vehicle sensors for multiple purposes - not only for their primary functions but also for inferring component health status. The same sensor data is analyzed through multiple algorithms to detect various failure modes, making the monitoring system universally applicable across different component types without adding dedicated sensors for each.
Solution Approach 2:
The system introduces an intermediary layer of analysis between raw sensor data and component status assessment. Machine learning models and diagnostic algorithms act as intermediaries that process sensor readings, correlate multiple data sources, and infer component health states that cannot be directly measured, thereby compensating for information gaps without adding physical sensors.
2Reliability
If more sensors are added to capture complete vehicle status information, then component health assessment improves, but system complexity and cost increase
Solution Approach 1:
The system replaces physical sensors with virtual sensing through data analysis. Instead of adding mechanical sensors to directly measure component wear or health, the system uses machine learning models that process existing sensor data to predict component status, effectively substituting physical measurement devices with computational analysis.
Solution Approach 2:
The system performs preliminary analysis of sensor data to predict potential component failures before they occur. By continuously monitoring trends and patterns in existing sensor readings, the system can forecast component degradation and alert operators in advance, enabling preventive maintenance without waiting for actual failures or adding redundant sensors.
3Loss of time
If comprehensive component monitoring is implemented, then early fault detection is possible, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the monitoring task into multiple levels - individual sensor monitoring, component-level analysis, and system-wide diagnostics. Each level processes data independently with appropriate complexity, allowing early fault detection at the component level without requiring all systems to process full computational loads simultaneously, thereby reducing overall processing complexity while maintaining fast detection capability.
Data Source
AI summary
A method for evaluating a status of a vehicle, the method may include (i) obtaining sensed information during one or more driving sessions of the vehicle; (ii) determining, based on the sensed information, (a) multi-component-model (MCM) behavioral information regarding one or more MCM driving events, and (b) a component behavioral information regarding one or more component driving events; wherein a behavior of at least one first part of the vehicle during the one or more MCM driving events is indicative of a status of one or more MCMs; wherein a behavior of at least one second part of the vehicle during the one or more component driving event is indicative of a status of one or more components; (iii) determining the status of the one or more MCMs, based at least on the MCM behavioral information; and (iv) determining the status of the one or more components, based at least on the component behavioral information, the status of the component.