Work Vehicle Implement Identification Using Multi-Signal Decision Trees
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Solution Overview
Problem
Existing methods fail to accurately distinguish a wide variety of implements connected to work vehicles, hindering effective predictive maintenance and service improvement.
Innovation Solution
A system that utilizes a work vehicle's internal signals to generate input data, which is processed by trained machine learning models, specifically decision trees, to estimate the type of implement connected to the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use basic operation information to estimate implement type, then the system complexity is low, but the measurement precision of implement type identification is insufficient
Solution Approach 1:
The system segments the implement identification process into multiple stages: data collection from multiple sensors, feature extraction, machine learning model processing, and classification. This segmentation allows complex identification tasks to be broken down into manageable components, improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw sensor data and implement type classification. These models act as mediators that process complex multi-sensor data and translate it into accurate implement type identification, resolving the contradiction between measurement precision and system complexity
2Measurement precision
If multiple internal signals are acquired and processed through trained models, then the measurement precision of implement type estimation is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline with extensive datasets before deployment. During actual operation, the pre-trained models can quickly classify implement types based on real-time sensor data, reducing online processing time while maintaining high accuracy
Solution Approach 2:
The patent selectively processes only the most relevant features from multiple sensor signals rather than analyzing all possible data points. This partial action approach maintains high identification accuracy while reducing the computational burden and processing time required
Data Source
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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.