Work Machine Damage Monitoring for Driven Member Life Prediction
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Solution Overview
Problem
Existing state display devices for work machines like excavators cannot predict the temporal changes in cumulative damage degrees during operation, leading to inadequate prediction of part life and potential operational risks.
Innovation Solution
A state management device equipped with a controller and sensors to compute and display the cumulative damage degree of driven members based on posture and action information, allowing for real-time monitoring and prediction of part life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the state display device only displays cumulative damage degree at maintenance time, then the device complexity is reduced, but the ability to predict part life is insufficient
Solution Approach 1:
The system performs preliminary computation of cumulative damage degree based on pre-collected operation status data and maintenance history, enabling life prediction before actual maintenance occurs. The controller calculates damage accumulation in advance using stored operation data, allowing proactive maintenance planning.
Solution Approach 2:
The display device provides continuous feedback on cumulative damage degree to the operator, showing both current damage levels and predicted remaining life. This feedback loop enables real-time monitoring and decision-making regarding maintenance timing, transforming static maintenance schedules into dynamic, data-driven decisions.
2Measurement precision
If temporal changes in cumulative damage degree are monitored continuously, then part life prediction is improved, but the loss of information processing capacity increases
Solution Approach 1:
The system extracts only the essential parameters needed for damage calculation from the vast amount of operation data, such as operation hours, load conditions, and maintenance intervals. By focusing on key damage-influencing factors rather than processing all available data, the system achieves precise damage monitoring with reduced computational burden.
Solution Approach 2:
The system transforms raw operation data into meaningful damage degree parameters through standardized calculations. By converting diverse operational conditions into a unified cumulative damage metric, the system simplifies data processing while maintaining measurement precision across different operating scenarios.
3Reliability
If maintenance is performed frequently to ensure reliability, then part failure risk is reduced, but the loss of time and productivity increases
Solution Approach 1:
The maintenance schedule transitions from a static, predetermined interval to a dynamic, condition-based approach. The system continuously updates the predicted remaining life based on actual damage accumulation rates, allowing maintenance timing to adapt to real-time machine conditions. This enables extending maintenance intervals when damage accumulation is slow while triggering earlier maintenance when damage rates increase.
Data Source
AI summary
A state management device manages the state of a work machine. The work machine includes a work device, a posture sensor that senses posture information regarding the work device, and an action sensor that senses action information regarding actuators. The state management device includes a controller that performs a computation on the basis of the results of sensing performed by the posture sensor and the action sensor, and an output device that outputs the result of computation performed by the controller, in a recognizable manner. On the basis of the posture information regarding the work device and the action information regarding the actuators, the controller computes the cumulative damage degree of a driven member, stores temporal changes in the cumulative damage degree at a predetermined position of the driven member, and outputs the temporal changes in the cumulative damage degree at the predetermined position to the output device.