Combine Harvester Control Interface for Remote Settings Adjustment
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Solution Overview
Problem
Current systems for monitoring and controlling combine harvesters face challenges in real-time performance assessment and setting adjustments, leading to inefficiencies in operations such as grain loss, productivity, and fuel economy, due to lack of immediate access to current machine settings and comparative performance data across multiple machines.
Innovation Solution
A control interface and system that detects operating conditions and performance metrics, allowing for automatic adjustments based on prioritized settings changes, utilizing sensors and network communication to provide real-time data and control inputs to optimize harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment and control inputs are required for combine settings, then operator control flexibility is maintained, but operation complexity and time consumption increase
Solution Approach 1:
The combine harvester system performs self-adjustment of operating settings by automatically detecting performance metrics and operating conditions, then modifying settings without operator intervention. The system monitors grain loss, productivity, and fuel economy metrics, and autonomously adjusts settings to optimize these parameters, eliminating the time-consuming manual adjustment process while maintaining operational effectiveness.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor performance metrics (grain loss, productivity, fuel economy) and operating conditions, feed this data to control algorithms, which then automatically adjust settings. This closed-loop feedback mechanism enables real-time optimization without requiring operator presence or manual intervention for settings changes.
2Loss of information
If multiple performance metrics are monitored simultaneously, then comprehensive performance assessment is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments performance monitoring into distinct functional modules: grain loss detection subsystem, productivity measurement subsystem, fuel economy monitoring subsystem, and settings optimization subsystem. Each module independently processes specific metrics and communicates with a central control system, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The control system is designed as a multi-functional platform that simultaneously processes multiple performance metrics (grain loss, productivity, fuel economy) and operating conditions through integrated algorithms. This universal system handles diverse data types and optimization goals within a single architecture, avoiding the need for separate specialized systems for each metric.
3Productivity
If automatic settings changes are implemented, then productivity and efficiency improve, but risk of incorrect adjustments and system reliability issues increase
Solution Approach 1:
The system performs preliminary analysis and validation of detected operating conditions and performance metrics before implementing settings changes. Control algorithms evaluate multiple scenarios and predict outcomes, selecting optimal adjustments only after verification that conditions warrant changes and that proposed adjustments will improve performance. This preliminary action reduces the risk of incorrect settings modifications.
Solution Approach 2:
The automatic control system implements dynamic, adaptive adjustment strategies that respond to real-time conditions rather than applying fixed rules. The system continuously learns from operational data, adjusts control parameters based on current context, and modifies settings incrementally to maintain stability. This dynamic approach enhances reliability by adapting to varying field conditions and machine states.
Data Source
AI summary
Operating conditions corresponding to a harvesting operation being performed by a mobile harvesting machine are detected along with a priority of a first performance pillar metric relative to a second performance pillar metric. An operating characteristic of the mobile harvesting machine is detected and a performance pillar metric value is identified for the first performance pillar metric based on the detected operating characteristic. A performance limitation corresponding to the first performance pillar metric is identified based on the detected operating conditions and an aggressiveness setting is detected that is indicative of an operating settings change threshold. It is then determined whether a settings change is to be performed based on the first performance pillar metric value, the priority of the first performance pillar metric, the first performance limitation and the settings change threshold and if the settings change is to be performed, a settings change actuator is controlled to execute the settings change.