Vehicle Component Condition Monitoring Using Image-Based Wear Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicle components, such as tires and brake pads, wear down over time, leading to decreased performance, safety, and potential damage to other vehicle parts if not replaced promptly, necessitating a method to monitor their condition effectively.
Innovation Solution
Implementing a system with onboard or remote image sensors that capture data on vehicle components, using machine learning models or physical sensors to determine conditions like wear, cracks, and punctures, and outputting this information to the vehicle or fleet management systems to control operations, such as limiting speed or navigating to a maintenance facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle components are monitored using traditional manual inspection methods, then operational costs and time consumption are reduced, but detection precision and reliability of component condition assessment deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection with automated image sensing systems. Image sensors capture visual data of vehicle components, and machine learning models automatically analyze this data to detect wear, cracks, and other conditions, eliminating the need for human inspectors to physically examine each component while significantly improving detection precision and consistency.
Solution Approach 2:
The patent creates visual copies (images) of vehicle components using image sensors. These digital copies are then analyzed by machine learning models to assess component conditions. This copying approach allows for non-contact, repeatable, and highly precise measurement of component states without the complexities of direct physical measurement or manual inspection.
2Loss of time
If traditional manual inspection methods are used for vehicle components, then system complexity is minimized, but loss of time for detecting worn components increases
Solution Approach 1:
The patent implements continuous monitoring of vehicle components through image sensors that can capture images of components as the vehicle moves or during scheduled stops. The machine learning model continuously processes these images to detect component degradation in real-time or near-real-time, eliminating the periodic interruptions required by manual inspections and significantly reducing the overall time lost to maintenance activities.
Solution Approach 2:
The monitoring system performs self-assessment of vehicle component conditions through automated image capture and analysis. The machine learning model independently evaluates component states without requiring human intervention, enabling the system to monitor its own operational status and automatically identify when maintenance is needed, thereby reducing the time vehicles are taken out of service for inspections.
3Reliability
If proactive maintenance is implemented through continuous monitoring, then vehicle safety and performance are improved, but use of energy and operational costs increase
Solution Approach 1:
The patent implements periodic monitoring rather than continuous monitoring, where image sensors capture component images at scheduled intervals or during specific vehicle operations (e.g., during charging stops for electric vehicles). The machine learning model processes these periodic images to assess component conditions. This approach maintains high reliability by regularly checking component states while significantly reducing energy consumption compared to continuous monitoring, as the system remains in low-power states between inspection cycles.
Data Source
AI summary
Techniques for determining a condition of a component of a vehicle and/or adjusting a configuration of a vehicle based on a condition of a component of the vehicle are discussed herein. The vehicle can receive vehicle component data and use the vehicle component data to determine a condition of one or more vehicle components. The vehicle can use a sensor, such as an image sensor, to capture or otherwise determine the vehicle component data. The condition of the component(s) of the vehicle may be determined using a machine learned model trained to determine condition of the component from image data. The vehicle can determine vehicle configuration data based on the condition. The vehicle configuration data may include an instruction to navigate to a maintenance facility, to remove the vehicle from service, constraining operation of the vehicle, etc.