Center Pivot Irrigation Analytics for Predictive Fault Detection
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Solution Overview
Problem
Modern center pivot irrigation systems are prone to malfunctions and maintenance issues due to their complexity and varied operator expertise, making it difficult to monitor and diagnose small problems before they become significant issues.
Innovation Solution
A system and method that integrates predictive and machine learning analytics to analyze sensor data from center pivot irrigation systems, providing real-time and historical data analysis, predictive maintenance patterns, and notifications to operators, combining geolocation and clock data to generate warnings and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If modern center pivot irrigation systems use multiple powered elements and sensors to control irrigation aspects, then irrigation control precision is improved, but system complexity increases making malfunctions harder to monitor and diagnose
Solution Approach 1:
The system segments monitoring functions by deploying multiple independent sensors (vibration, temperature, pressure, flow, electrical current) that each monitor specific parameters. This segmentation allows individual sensor failures to be isolated without affecting the entire system, while collectively providing comprehensive monitoring coverage.
Solution Approach 2:
A central control system acts as an intermediary that collects data from all sensors and powered elements, processes the information, and coordinates responses. This intermediary consolidates the complexity of multiple components into a single management point, making the system more manageable despite its complexity.
2Adaptability or versatility
If irrigation systems are designed for use by multiple operators with varied technical experience, then system versatility is improved, but maintenance issue detection capability deteriorates
Solution Approach 1:
The system performs self-diagnosis by automatically monitoring its own parameters through sensors and comparing readings against normal operating ranges. This self-service capability allows the system to detect maintenance issues without requiring operator expertise, compensating for varied technical experience levels.
Solution Approach 2:
The control system continuously receives feedback from sensors and provides real-time alerts when parameters deviate from normal ranges. This automated feedback loop ensures consistent issue detection regardless of operator skill level, as the system itself identifies and reports problems.
3Ease of operation
If small maintenance issues are not monitored until they become significant repair issues, then operational simplicity is maintained, but system reliability deteriorates
Solution Approach 1:
The system performs preliminary monitoring of maintenance parameters continuously during normal operation. By detecting early signs of wear or malfunction through sensor data analysis, the system enables preventive maintenance before small issues develop into significant repairs, improving reliability without complicating operation.
Solution Approach 2:
The system replaces manual inspection with automated electronic sensing and data processing. Sensors continuously monitor parameters that would require physical inspection, and the control system automatically analyzes data to predict maintenance needs, eliminating the need for operators to manually detect early signs of failure.
4Speed
If sensor data is processed and stored locally in the irrigation machine, then response time is improved, but data storage requirements and processing load increase
Solution Approach 1:
The system extracts and stores only critical maintenance parameters and alarm conditions in local memory, while transmitting comprehensive data sets to remote servers for long-term archival. This extraction approach enables fast local response to immediate issues while offloading bulk storage requirements to external systems.
Data Source
AI summary
The present invention provides a system and method for analyzing sensor data related to an irrigation system. According to a preferred embodiment, the system includes algorithms for analyzing real-time, near real-time and historical data acquired from sensors in communication with a mechanized irrigation machine. Further, the algorithms of the present invention system may analyze collected sensor data to determine if an event has occurred or is predicted to occur. Further, the algorithms of the present invention may provide commands to an irrigation machine and notifications to users. According to further aspects of the present invention, the algorithms of the present invention may preferably apply machine learning and other data analysis tools to detect maintenance patterns, geographic trends, environmental trends, and to provide predictive analysis for future events.