Agricultural Machine Fault Cause Analysis Using Operating and Design Data
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Solution Overview
Problem
Current methods for determining the cause of faults in agricultural machines are inefficient and costly, often relying on retrospective experience without data-driven analysis, leading to prolonged downtime and unnecessary part replacements during critical harvest periods.
Innovation Solution
A method using operational and design data from agricultural machines, combined with environmental and test data, to identify whether utilization or design issues contribute to faults, employing a computer system for data aggregation and analysis to distinguish between random and systematic effects, and isolate failure causes, including error chains and material defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If retrospective experience-based methods are used to determine fault causes, then diagnostic simplicity is maintained, but diagnostic accuracy and reliability deteriorate due to lack of actual fault data
Solution Approach 1:
The system collects and stores operational data, environmental data, and fault data in advance during normal machine operation. This preliminary data accumulation enables accurate fault cause identification when faults occur, without requiring complex real-time analysis during diagnostic procedures
Solution Approach 2:
A computer system acts as an intermediary between raw data collection and fault diagnosis. The computer system processes operational data, environmental data, and fault data to generate meaningful fault cause information, simplifying the diagnostic process while maintaining high accuracy
2Measurement precision
If comprehensive data collection and analysis systems are implemented, then fault cause identification accuracy improves, but device complexity and cost increase
Solution Approach 1:
The system collects multiple types of data (operational data, environmental data, fault data) using a unified data collection framework. This multi-functional approach enables the system to handle various fault scenarios with a single integrated system, managing complexity while maintaining comprehensive analysis capabilities
Solution Approach 2:
The system automatically processes and analyzes collected data without requiring manual intervention. The computer system autonomously identifies fault causes by processing operational data, environmental data, and fault data, reducing the need for complex manual diagnostic procedures
3Productivity
If data-driven fault analysis is performed, then repair time and downtime are reduced, but loss of time for data collection and processing increases
Solution Approach 1:
Operational data, environmental data, and fault data are collected and stored in advance during normal machine operation. When faults occur, the pre-collected data is immediately available for analysis, eliminating data collection delays during repair scenarios
Solution Approach 2:
The system continuously monitors machine operation and feeds operational data back to the database. This continuous feedback loop ensures data is always current and ready for immediate analysis when faults occur, minimizing data processing time during repairs
4Measurement precision
If aggregated data from multiple machines is analyzed, then identification of systematic faults and hidden interdependencies improves, but quantity of data to be processed increases
Solution Approach 1:
The system is designed to handle multiple data types (operational data, environmental data, fault data) from multiple machines within a unified framework. This universal approach enables systematic fault identification across the machine fleet without requiring separate processing systems for each data type
Solution Approach 2:
The computer system serves as an intermediary that processes aggregated data from multiple machines. It identifies systematic faults and hidden interdependencies by analyzing patterns across the data, managing the complexity of large data volumes while maintaining high root cause identification capability
Data Source
Figure 1
AI summary
The invention relates to a method for determining the cause of a fault in an agricultural machine (1) with a defective component (4), wherein an analysis routine is used based on operating data of the agricultural machine (1) comprising utilization data, at least design data of the defective component (4) and design data assigned to the agricultural machine (1), and fault cause data, wherein the analysis routine determines whether a utilization and/or a design of the agricultural machine (1), in particular of the defective component (4), can be excluded and/or identified as a cause of the fault.