Implement State Monitoring Using Pull Vectors and Soil Images
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Solution Overview
Problem
Farming vehicle operators face challenges in monitoring implement wear and state during automated operations, leading to potential damage and improper functioning due to insufficient monitoring, as they cannot always be present to check the condition of implements like plows or sprayers.
Innovation Solution
An implement management system that uses sensors and machine learning models to detect implement wear and state by measuring orientation vectors and analyzing images, allowing for real-time modification of vehicle operating modes to prevent damage and ensure proper operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated farming operations are implemented to allow operators to perform other tasks, then operator productivity increases, but implement monitoring capability deteriorates leading to undetected wear and potential damage
Solution Approach 1:
The implement management system enables the implement to monitor itself through integrated sensors that automatically detect wear conditions and operational states without requiring operator intervention. The system self-diagnoses issues by comparing sensor data against expected parameters and automatically generates alerts or modifies vehicle operations to prevent damage.
Solution Approach 2:
The patent replaces manual visual inspection with automated electronic sensing systems. Sensors such as force sensors, angle sensors, and cameras substitute for the operator's eyes and manual checking, converting mechanical monitoring into electronic detection and analysis.
2Reliability
If operators continuously monitor implement conditions during operation, then implement reliability is maintained, but operator workload and time consumption increase
Solution Approach 1:
The system continuously collects sensor data and provides real-time feedback about implement conditions. The feedback mechanism compares actual sensor readings against expected parameters and immediately alerts the operator or automatically adjusts operations when wear or abnormal conditions are detected, eliminating the need for continuous manual monitoring.
Solution Approach 2:
The monitoring system operates autonomously without requiring operator attention or time investment. The sensors, processing unit, and control system work together to self-monitor implement conditions, freeing the operator to focus on other tasks while maintaining implement reliability.
3Measurement precision
If more sensors and monitoring systems are added to detect implement wear, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent employs multi-functional sensors that serve multiple purposes. For example, force sensors not only detect implement wear but also monitor soil resistance and operational loads. The same sensor data is used for both diagnostic purposes and operational control, reducing the need for separate specialized sensors.
Solution Approach 2:
The system combines multiple sensing functions into an integrated monitoring platform. The processing unit consolidates data from various sensors (force, angle, cameras) and performs multiple analysis functions, merging what would otherwise be separate monitoring systems into a unified solution that reduces overall complexity.
Data Source
AI summary
An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.