Load Cell Residual Fatigue Life Estimation
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Solution Overview
Problem
Existing load cells face challenges in predicting residual fatigue life due to randomly varying load peaks and cycles, which hinders proactive maintenance and system availability.
Innovation Solution
A system and method that includes an in-service load cell with a sensor, on-board memory, and processor to detect peak loads, calculate load cycles, and estimate residual life using fatigue life reference data, allowing for real-time feedback on load cell health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If load cells operate under randomly varying load peaks and cycles, then the load cell can serve diverse operational requirements, but the ability to predict residual life becomes significantly more difficult
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and recording load peak data during operation, accumulating fatigue damage information in real-time. This preliminary data collection and cumulative damage calculation enable accurate residual life prediction when queried, transforming the difficulty of predicting under random loads into a manageable process through continuous preparatory monitoring.
2Reliability
If real-time residual life estimation is implemented, then system availability improves through proactive maintenance, but the device complexity increases due to additional monitoring and calculation components
Solution Approach 1:
The load cell performs self-service by utilizing its own existing sensor to detect load peaks and processing its own output signal to calculate cumulative damage. The in-service load cell monitors itself and provides its own residual life estimation without requiring separate external monitoring equipment, thereby improving reliability through proactive maintenance capability while minimizing additional device complexity.
3Measurement precision
If cumulative damage calculation is performed continuously throughout the load cell life, then accurate residual life information is available for maintenance planning, but the processing requirements and computational load increase
Solution Approach 1:
The system replaces complex continuous computational processing with a simplified lookup-based approach. Instead of continuously performing complex fatigue calculations, the processor stores pre-calculated S-N curve data and uses simple array indexing and multiplication operations to estimate residual life. This substitution of mechanical/computational complexity with data storage and simple retrieval maintains measurement precision while significantly reducing processing energy consumption.
Data Source
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AI summary
A system and method for estimating the residual fatigue life of a load cell includes storing, in a memory, fatigue life reference data associated with a test load cell. A predetermined number of data storage bins are provided. Each data storage bin is representative of a predefined value range of peak loads. Peak loads applied to an in-service load cell are detected, and each is stored in an appropriate one of the data storage bins. A number of load cycles of the in-service load cell are calculated based on the detected peak loads stored in each of the data storage bins. An estimate of the residual life of the in-service load cell is calculated from the fatigue life reference data and the calculated number of load cycles of the in-service load cell.