Agricultural Machinery Predictive Maintenance via Contextual ML
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Solution Overview
Problem
Current machinery maintenance systems lack consideration for contextual variables such as geographical region, terrain, and specific use conditions, leading to inadequate predictive maintenance for agricultural machinery, resulting in inefficient maintenance schedules and increased downtime.
Innovation Solution
The development of improved machine learning models that incorporate contextual variables like crop type, past work orders, and geographical information to provide personalized predictive maintenance recommendations for agricultural machinery, including step-by-step guidance for repairs and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional maintenance systems are used without contextual variables, then the system complexity is low, but the maintenance prediction accuracy is insufficient
Solution Approach 1:
The system collects and stores contextual variables (geographical region, terrain, crop type, work history) in advance before maintenance is needed. This preliminary data gathering enables more accurate predictions when the maintenance evaluation is performed, resolving the contradiction by preparing information beforehand rather than adding complex real-time analysis.
Solution Approach 2:
The patent introduces an intermediary layer (the contextual variable database and machine learning model) that bridges the gap between simple maintenance tracking and accurate prediction. This intermediary processes contextual information to generate maintenance recommendations, improving accuracy without requiring the entire system to become exponentially more complex.
2Productivity
If generic maintenance schedules are followed without contextual considerations, then the ease of operation is high, but the machinery downtime is increased
Solution Approach 1:
The maintenance schedule transitions from a static, generic timetable to a dynamic plan that adapts based on contextual variables. The system continuously evaluates geographical region, terrain, crop type, and work history to adjust maintenance timing and recommendations, thereby reducing downtime while maintaining operational simplicity through automated adjustments.
Solution Approach 2:
The system performs self-adjustment by automatically incorporating contextual variables and generating customized maintenance recommendations without requiring manual intervention. This self-service capability improves machinery uptime by optimizing maintenance schedules while keeping the operation simple for users who receive ready-made recommendations.
3Measurement precision
If contextual variables are incorporated into machine learning models, then the maintenance prediction accuracy is improved, but the data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant contextual variables (geographical region, terrain, crop type, work history) from the available data, rather than processing all possible data points. This selective extraction maintains high prediction accuracy while reducing the overall data volume that needs to be processed and stored.
Solution Approach 2:
The patent applies different levels of data collection and processing to different contextual variables based on their relevance to specific machinery types and operating conditions. Not all variables are processed with equal depth everywhere - the system adapts the data processing intensity to local requirements, reducing overall data volume while maintaining accuracy where it matters most.
Data Source
AI summary
This document provides systems, methods, and processes for determining an evaluation of a machine. An example method performed by one or more computers, can include receiving, from a database, input data corresponding to an agricultural machine; determining, from the input data, a plurality of attributes that is associated with a respective characteristic or component of the agricultural machine; determining, by a machine learning model, an evaluation of the agricultural machine by applying a set of parameters of the machine learning model on the plurality of attributes, wherein the evaluation includes a prediction regarding respective repairs or maintenances of one or more components of the agricultural machine; and displaying an extended reality representation of the evaluation on a user interface, the extended reality representation including one or more user interface indicators that each represents performing a respective predicted repair or maintenance of the one or more components of the agricultural machine.