Vehicle Dynamic Modeling for Early Tire and Chassis Fault Detection
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Solution Overview
Problem
Current vehicle condition monitoring systems struggle to accurately detect subtle faults such as reduced tire pressure, shock absorber issues, and chassis faults, as these changes are not immediately registered and can be difficult to identify.
Innovation Solution
A computer-implemented method and apparatus that applies a dynamic model to a vehicle by combining real-time status information from information buses, vehicle dynamics data, and map data to analyze behavior, estimate performance, and detect changes in vehicle characteristics, including tire and chassis conditions, using calibration parameters and road characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to detect vehicle faults, then the system structure is simple, but the measurement precision for detecting subtle faults such as reduced tire pressure and shock absorber issues is insufficient
Solution Approach 1:
The monitoring system is segmented into multiple independent functional modules: a dynamic model construction module that creates virtual vehicle models, a data acquisition module that collects sensor data, a comparison module that analyzes deviations, and a fault detection module that identifies specific issues. This segmentation allows each module to specialize in one aspect of monitoring, improving overall detection precision while keeping individual modules manageable in complexity
Solution Approach 2:
The system performs preliminary action by constructing a dynamic model of the vehicle's normal behavior patterns before actual fault detection occurs. This pre-established model serves as a reference baseline that enables the system to immediately identify deviations when faults occur, eliminating the need for complex real-time analysis algorithms and reducing system complexity while maintaining high measurement precision
2Measurement precision
If dynamic modeling with multiple data sources is implemented to improve monitoring accuracy, then the measurement precision increases, but the device complexity increases due to multiple information buses and data processing requirements
Solution Approach 1:
The dynamic model serves multiple functions simultaneously: it represents the vehicle's mechanical behavior, predicts sensor readings under normal conditions, identifies fault patterns, and adapts to different driving scenarios. This multi-functionality consolidates what would otherwise require separate systems into a single unified model, improving monitoring accuracy without proportionally increasing system complexity
Solution Approach 2:
The dynamic model acts as an intermediary layer between the raw sensor data from multiple information buses and the fault detection logic. Instead of directly comparing data from numerous sensors, the system uses the dynamic model to translate sensor readings into meaningful behavioral parameters, simplifying the data processing architecture while enhancing monitoring precision
3Reliability
If real-time status information from information buses is continuously analyzed, then the reliability of fault detection improves, but the loss of time for data processing and computation increases
Solution Approach 1:
The system implements periodic action by analyzing data at strategically determined intervals rather than continuously processing every sensor reading. The dynamic model predicts expected sensor values and only triggers detailed analysis when actual readings deviate beyond a threshold, maintaining high detection reliability while dramatically reducing average data processing time
Solution Approach 2:
The system applies partial action by focusing computational resources only on the most critical parameters and sensors relevant to specific fault types. Instead of analyzing all sensor data equally, the dynamic model identifies and monitors only the key indicators of vehicle health, achieving reliable fault detection with reduced computation time and resource requirements
Data Source
AI summary
According to an aspect, there is provided a computer-implemented method for condition monitoring of a vehicle. The method comprises applying a dynamic model associated with a vehicle (800), the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle (200), obtaining, based on vehicle identity information, vehicle dynamics information (202), obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data (204), analyzing behavior of the vehicle based on the status information and the vehicle dynamics information (206), and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data (208); analyzing historical changes in at least one calibration parameter associated with the dynamic model of the vehicle (802); analyzing effects of the three-dimensional road map associated with the roads and the road characteristics data in at least one position on the behavior of the vehicle (804); and determining, based on the analyzed historical changes and the effects, at least one change in at least one vehicle characteristic (806).