Center Pivot Irrigation Analytics for Predictive Maintenance
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Solution Overview
Problem
Modern center pivot irrigation systems are prone to malfunctions and maintenance issues due to their complexity, which are often unnoticed until they become significant problems, especially given the varied technical experience of operators.
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, geographic trends, and environmental trends, along with notifications and alarms to operators, both locally and remotely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If modern center pivot irrigation systems use multiple powered elements and sensors to control various aspects of irrigation, then the functionality and control precision are improved, but the system complexity increases and makes the system prone to malfunctions and maintenance issues
Solution Approach 1:
The patent combines multiple sensors (vibration, temperature, humidity, pressure sensors) and powered elements into an integrated monitoring system that collects data from all components through a centralized controller, allowing unified management of the complex irrigation system
Solution Approach 2:
The patent introduces a centralized controller and monitoring system as an intermediary between the various sensors and powered elements, which processes data from multiple sources and provides coordinated control, simplifying the operational complexity while maintaining precise control
2Adaptability or versatility
If irrigation machines are designed with multiple independent powered elements and sensors, then the operational capabilities are improved, but the reliability decreases due to increased malfunctions and maintenance issues
Solution Approach 1:
The patent implements continuous feedback loops where sensors monitor the status of powered elements and the controller adjusts operations in real-time, detecting anomalies and preventing failures before they occur, thereby maintaining high reliability despite system complexity
Solution Approach 2:
The patent uses predictive analytics and continuous monitoring to detect early signs of component failure, allowing maintenance to be performed before actual malfunctions occur, thus preventing reliability degradation in systems with multiple powered elements
3Adaptability or versatility
If irrigation systems use remote and independent power for multiple sensors and control systems, then the operational independence and control flexibility are improved, but the difficulty of detecting and measuring maintenance issues increases
Solution Approach 1:
The patent merges data from all independent sensors and powered elements into a centralized monitoring platform that provides unified visibility into system status, making it easier to detect maintenance issues across the distributed system
Solution Approach 2:
The centralized controller acts as an intermediary that aggregates data from all remote sensors and powered elements, providing a single point for monitoring and detecting maintenance issues across the entire irrigation system
4Ease of operation
If small maintenance issues are not monitored due to varied operator experience, then the operational simplicity is maintained, but the loss of time occurs when issues become significant repair problems
Solution Approach 1:
The system performs self-diagnosis through automated monitoring and analytics, detecting maintenance issues without requiring operator expertise, thereby maintaining operational simplicity while preventing significant downtime through early intervention
Solution Approach 2:
The continuous feedback mechanism automatically detects and alerts operators to maintenance issues before they become critical problems, eliminating the need for operator expertise in early detection and preventing time loss from major failures
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.