Multi-Axis Robot Torque Segmentation for Failure Diagnosis
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Solution Overview
Problem
Conventional failure diagnostic methods for multi-axis robots are inadequate as they do not consider the varying disturbance torques caused by different operational contents, leading to inaccurate failure diagnoses.
Innovation Solution
A failure diagnostic device and method that groups disturbance torques according to the specific operations executed by the robot and compares each grouped torque with tailored thresholds, ensuring accurate diagnosis by accounting for the operational context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a preset threshold is used for disturbance torque comparison without considering operation contents, then the diagnostic method is simple, but the diagnosis accuracy deteriorates due to false positives
Solution Approach 1:
The patent segments the disturbance torque data by grouping it according to operation contents (e.g., operation types, load conditions, speed ranges). Instead of using a single unified threshold, the system creates multiple thresholds corresponding to different operation segments. This segmentation allows accurate comparison by matching the appropriate threshold with the current operation context, thereby resolving the contradiction between simplicity and accuracy.
2Measurement precision
If disturbance torques are grouped according to operation contents, then diagnosis accuracy is improved, but system complexity increases
Solution Approach 1:
The system continuously monitors operation contents and dynamically selects or adjusts thresholds based on real-time feedback about the current operation state. This feedback mechanism allows the system to adapt to changing conditions without requiring complex manual reconfiguration, thereby improving diagnosis accuracy while keeping the system manageable through automated adaptation.
Solution Approach 2:
The patent changes the threshold parameter based on operation contents such as operation type, load condition, and speed range. By making the threshold a variable parameter rather than a fixed value, the system can accurately reflect the actual disturbance characteristics under different operating conditions, thereby improving diagnosis accuracy without requiring overly complex diagnostic logic.
Data Source
AI summary
A failure diagnostic device includes a torque detector that detects disturbance torques applied to joint shafts included in a multi-axis robot, a torque grouping circuit that groups the disturbance torques according to a content of an operation executed by the multi-axis robot upon detection of each disturbance torque, a torque correction circuit that obtains a corrected disturbance torque standardized between a plurality of operations with different contents based on a representative value preliminarily set for each grouped disturbance torque and the disturbance torque detected by the torque detector, and a failure diagnostic circuit that performs a failure diagnosis on the multi-axis robot by comparing the corrected disturbance torque with a threshold.