Robot Arm Vibration Monitoring for Predictive Component Failure
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Solution Overview
Problem
Existing failure prediction methods for robot components, such as reducers, require disassembly and manual inspection, which is labor-intensive and inconvenient.
Innovation Solution
A failure prediction method and apparatus that utilize machine learning to generate a failure prediction model based on vibration characteristics detected by sensors, allowing for predictive maintenance without disassembly, using a neural network to analyze data from encoders, inertial sensors, and other detection units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iron powder concentration data of grease is used for failure prediction, then prediction accuracy is improved, but maintenance complexity and time consumption increase due to required disassembly
Solution Approach 1:
The patent replaces mechanical disassembly and manual inspection methods with non-contact vibration sensing and machine learning-based prediction. Vibration sensors mounted on the robot arm detect vibration characteristics that correlate with component degradation, eliminating the need to disassemble the robot arm and check grease iron powder concentration manually.
Solution Approach 2:
The patent introduces vibration characteristics as an intermediary parameter that indirectly reflects component health status. Instead of directly measuring iron powder concentration through disassembly, the system uses vibration signals as a mediator to infer degradation state, enabling non-intrusive monitoring.
2Reliability
If manual inspection through disassembly is performed, then direct component status is obtained, but productivity decreases due to operational disruption
Solution Approach 1:
The patent enables continuous monitoring of component health through ongoing vibration data collection during robot operation. The machine learning model continuously processes vibration characteristics to track degradation trends, allowing maintenance planning without interrupting production workflows.
Solution Approach 2:
The system performs preliminary failure prediction by analyzing vibration trends before actual component failure occurs. The machine learning model estimates remaining useful life and predicts future failure points, enabling proactive maintenance scheduling that prevents operational disruptions rather than reacting to failures.
3Ease of operation
If vibration characteristics are used for prediction, then ease of operation is improved by eliminating disassembly, but measurement complexity increases due to data processing requirements
Solution Approach 1:
The system implements self-service through automated machine learning-based prediction. The model automatically processes vibration data, identifies degradation patterns, and generates failure predictions without requiring expert intervention or complex manual analysis procedures.
Solution Approach 2:
The patent transforms complex vibration signals into simplified degradation indicators through machine learning feature extraction. The model converts multi-dimensional vibration data into meaningful parameters such as degradation level and predicted failure time, reducing data complexity while preserving predictive information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of component failures in robot arms without disassembly, providing timely maintenance recommendations and reducing operational disruptions.
Implementation Method 1
a detection section that detects information on vibration characteristics when the robot arm moves
Data Source
AI summary
A failure prediction method of predicting a failure of a component of a robot including a robot arm having the component and a detection section that detects information on vibration characteristics when the robot arm moves, includes generating a failure prediction model for prediction of the failure of the component by machine learning based on the information on vibration characteristics, and predicting the failure of the component based on an estimated value of failure prediction output by the generated failure prediction model when the information on vibration characteristics is input to the generated failure prediction model.