Agricultural Machine Anomaly Detection Without Custom Programming
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Solution Overview
Problem
Existing agricultural monitoring systems require complex programming and parameter adjustments for each application, leading to unreliable functioning and inefficiencies in detecting anomalies during work processes.
Innovation Solution
Employing a deep learning network with an adaptive neural network to detect anomalies in image signals from electro-optical sensors, using pre-processing to extract features and subsequent analysis for reliable anomaly detection, reducing the need for application-specific programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex programming and parameter adjustments are used for each application, then the monitoring system can be customized for specific tasks, but the system reliability decreases and functioning becomes unreliable
Solution Approach 1:
The evaluation device automatically adapts to different work processes and sensors without requiring external programming or parameter adjustment. The system performs self-configuration and self-optimization by autonomously analyzing the specific application context and adjusting its monitoring parameters accordingly, eliminating the need for complex manual customization while maintaining high reliability
Solution Approach 2:
The evaluation device is designed as a universal monitoring system that can handle multiple different work processes and sensor types through a single standardized interface. The device incorporates multiple evaluation algorithms that can automatically detect and adapt to the specific application, providing consistent reliable functioning across diverse agricultural monitoring tasks without requiring application-specific programming
2Adaptability or versatility
If complex programming is required for each application, then specific monitoring requirements can be met, but the ease of operation deteriorates due to programming and parameter adjustments
Solution Approach 1:
The evaluation device automatically configures itself for different applications by detecting the work process type and sensor characteristics, then autonomously selecting appropriate evaluation algorithms and parameters. This self-configuration capability eliminates the need for operators to perform complex programming or parameter adjustments, making the system as easy to operate as simply activating the monitoring function
Solution Approach 2:
The system pre-configures multiple evaluation algorithms and parameter sets that are ready to be automatically selected based on the detected application context. During operation, the evaluation device automatically chooses the appropriate pre-configured algorithm without requiring real-time programming, thereby maintaining ease of operation while providing application-specific monitoring capabilities
Data Source
AI summary
An apparatus for monitoring an agricultural work machine, comprising: at least one sensor configured to record a work process of the agricultural work machine and generate an image signal based thereon; and an evaluation device configured to evaluate the image signal of the sensor based on comparison values and to generate an output value in the event of a detected problem, the evaluation device further configured with a deep learning network to detect anomalies in the image signal.