Work Vehicle Implement Identification Using Multi-Signal ML Estimation
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Solution Overview
Problem
Existing methods struggle to accurately distinguish the type of implement connected to a work vehicle, which is crucial for improving maintenance and service quality, as they lack the capability to identify a wide variety of implements accurately.
Innovation Solution
A system that utilizes a communication device on the work vehicle to transmit multiple signals to a server, which processes and analyzes these signals using trained machine learning models, particularly decision trees, to estimate the type of implement connected to the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to estimate implement type, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing operation information (engine revolutions, PTO clutch revolutions, vehicle speed, implement elevation angle) and position information before the estimation process. This pre-collected data is then used by the machine learning model to accurately estimate implement type, reducing the need for complex real-time sensing while maintaining high precision.
Solution Approach 2:
The management server acts as an intermediary that receives operation and position information from the work vehicle, processes this data through machine learning models, and generates implement type estimates. This intermediary approach allows the complex processing to occur remotely, reducing the complexity of the on-vehicle system while maintaining high measurement precision.
2Measurement precision
If multiple operation information parameters are collected, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system extracts only the most relevant operation information parameters needed for implement type estimation, such as engine revolutions, PTO clutch revolutions, vehicle speed, and implement elevation angle. By selecting and extracting only these key parameters rather than collecting all possible vehicle data, the system maintains high measurement precision while reducing data transmission load and information loss.
3Productivity
If machine learning models are deployed on work vehicles, then productivity is improved, but device complexity increases
Solution Approach 1:
The management server serves as an intermediary that hosts and executes the machine learning models remotely. The work vehicle only needs to collect and transmit operation information to the server, which then performs the complex estimation processing and returns results. This approach improves productivity by enabling accurate implement type identification for better maintenance planning, while avoiding the need to deploy complex machine learning infrastructure on the work vehicles themselves.
Data Source
AI summary
A method to be executed by one or more computers configured or programmed to communicate with a work vehicle to execute work by driving an implement connected to the work vehicle includes repeatedly acquiring, from the work vehicle, 10 or more signals respectively indicating different internal states of the work vehicle, generating input data based on the 10 or more signals; estimating a type of the implement by inputting the input data to one or more trained models usable to estimate the type of the implement based on the input data, and generating output data including information indicating the estimated type of the implement and outputting the output data.