Robot Drive Residual Life Prediction From Servo Current Trends
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods struggle to accurately predict the residual life of a robot drive system, making it difficult to plan maintenance schedules and perform timely maintenance, leading to potential robot breakdowns and production line stoppages.
Innovation Solution
A robot maintenance assist device that acquires data from servo motors, diagnoses future trends using a tendency diagnosis unit, and determines residual life by analyzing current command values, enabling accurate prediction and timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If residual life prediction is based on design life and operation time, then maintenance planning becomes simpler, but prediction accuracy deteriorates due to differences between assumed and actual operating conditions
Solution Approach 1:
The patent changes the parameters used for residual life prediction from static design life and operation time to dynamic parameters including actual operating conditions, vibration data, temperature, and load patterns. This allows the prediction system to adapt to real-world variations and maintain accuracy across different operating scenarios.
Solution Approach 2:
The patent replaces simple time-based mechanical counting with advanced diagnostic systems that use sensors, data acquisition units, and analysis algorithms to monitor actual component conditions. This substitution enables accurate prediction by replacing crude mechanical metrics with sophisticated electronic monitoring and analysis.
2Ease of operation
If maintenance is performed based on current data only, then immediate maintenance decisions can be made, but future maintenance timing cannot be specified
Solution Approach 1:
The patent performs preliminary analysis of current component conditions and establishes degradation trends before failure occurs. By analyzing historical data and current state, the system predicts future maintenance needs in advance, allowing maintenance teams to prepare and schedule interventions proactively rather than reactively.
Solution Approach 2:
The patent implements a feedback mechanism where actual component performance data is continuously monitored, compared against predicted degradation models, and used to update maintenance predictions. This closed-loop system refines accuracy over time and provides ongoing guidance for optimal maintenance timing.
3Productivity
If robot breakdown is allowed to occur, then continuous production is maintained, but production line stops and productivity declines
Solution Approach 1:
The patent applies preliminary anti-action by predicting component failures before they occur and scheduling maintenance interventions in advance. This preventive approach counteracts potential breakdowns before they can disrupt production, maintaining both productivity and reliability by eliminating the risk of unexpected failures.
Solution Approach 2:
The patent creates a cushion of predicted residual life for critical components, allowing maintenance to be scheduled during planned downtime rather than forcing unexpected production stops. This cushioning effect protects the production system from the harmful impact of sudden failures.
Data Source
Figure 1
Figure 2~4
Figure 5
AI summary
This device includes an acquired data storing unit (4) for storing acquired data about a current command value of a servo motor configuring a robot drive system (R1); a tendency diagnosis unit (5) for diagnosing a future changing tendency of the current command value based on the data of the current command value stored in the acquired data storing unit (4); and a life determining unit (6) for determining a term until the current command value reaches a previously set value based on the future changing tendency of the current command value acquired by the tendency diagnosis unit (5). Thus, a residual life of the robot drive system can be accurately predicted.