Robot Speed Reducer Diagnosis Using Maintenance History Correlation
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Solution Overview
Problem
Existing abnormality diagnosis devices cannot recognize cause-and-effect relationships between target apparatuses when an abnormality occurs, limiting their ability to predict and notify issues in associated movable parts.
Innovation Solution
An abnormality diagnosis device with a maintenance history storage unit and control unit that analyzes maintenance data to predict and notify abnormalities in associated movable parts by correlating maintenance data and sensor data from multiple parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maintenance data from multiple movable parts is collected and analyzed, then the accuracy of abnormality prediction in associated parts is improved, but the complexity of the diagnosis device and data processing increases
Solution Approach 1:
The diagnosis device is segmented into specialized functional units: a maintenance history storage unit for data retention, a control unit for diagnosis execution, and an abnormality prediction unit for predictive analysis. This segmentation allows each unit to handle specific tasks efficiently, improving prediction accuracy while managing overall system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by collecting and storing maintenance history data from multiple movable parts before abnormalities occur. The maintenance history storage unit accumulates data on maintenance timing, content, and results, enabling the abnormality prediction unit to analyze patterns and predict future failures proactively rather than reactively.
2Reliability
If maintenance history data is stored for each movable part, then the ability to predict abnormalities in associated parts is improved, but the amount of data storage and processing required increases
Solution Approach 1:
The maintenance history storage unit is designed with multi-functionality to store diverse types of maintenance data (timing, content, results) for multiple movable parts in a unified structure. This universal storage approach enables the system to handle various data types efficiently, improving prediction reliability while optimizing storage utilization through consolidated data management.
Solution Approach 2:
The control unit acts as an intermediary between the maintenance history storage unit and the abnormality prediction unit. It processes raw maintenance data, extracts relevant features, and prepares structured information for prediction analysis, thereby reducing the data processing burden on the prediction unit and optimizing the overall data flow efficiency.
Data Source
AI summary
An abnormality diagnosis device diagnoses an abnormality of a plurality of speed reducers included in a robot in accordance with disturbance torque regarding a state of the respective speed reducers acquired from a sensor installed in the robot, and outputs a result of the diagnosis to a display unit, the abnormality diagnosis device including a maintenance history DB configured to store maintenance data on maintenance made for the respective speed reducers, and a control unit configured to detect an abnormality in the respective speed reducers in accordance with the disturbance torque. The control unit, when detecting an abnormality in one speed reducer in accordance with the disturbance torque, predicts an abnormality in another speed reducer caused in association with the abnormality in the one speed reducer in accordance with the maintenance data, and outputs information on the predicted abnormality to the display unit.