Multi-Robot Maintenance Scheduling Using Failure Prediction
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Solution Overview
Problem
Existing control systems for robots fail to efficiently manage maintenance times across multiple robots, leading to varying maintenance schedules and reduced productivity due to insufficient consideration of component failure estimates.
Innovation Solution
A control apparatus that includes a failure prediction part to estimate component failure times, a maintenance time adjustment part to synchronize maintenance schedules based on predicted failures, and a load adjustment part to balance workloads of robots until maintenance times, ensuring that all robots are activated at the same time for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance times are adjusted individually for each robot based on predicted component failures, then the reliability of each robot is improved, but the productivity of the overall system deteriorates due to varying maintenance schedules causing downtime
Solution Approach 1:
The patent merges maintenance operations across multiple robots by synchronizing maintenance times. The maintenance time adjustment part coordinates maintenance schedules of multiple robots so that they undergo maintenance simultaneously or in an optimized sequence, combining what would otherwise be separate maintenance events into unified operations that reduce overall system downtime and improve productivity while maintaining reliability.
2Productivity
If the workload of robots is increased to maximize productivity, then the productivity improves, but the time to component failure decreases
Solution Approach 1:
The patent applies dynamics by making the workload adjustment flexible and time-dependent. The load adjustment part dynamically modifies robot workloads based on predicted failure times and maintenance schedules. Workloads are increased when robots are healthy and productive, then gradually reduced as predicted failure approaches, and adjusted again after maintenance. This dynamic adjustment optimizes productivity throughout the component lifecycle while preventing failures.
Solution Approach 2:
The patent applies preliminary action by predicting component failures before they occur and proactively adjusting workloads and scheduling maintenance in advance. The failure prediction part identifies potential failures ahead of time, allowing the system to prepare by reducing workloads before failure occurs and scheduling maintenance during optimal windows, preventing unexpected downtime and maintaining productivity.
3Reliability
If maintenance is performed frequently to ensure reliability, then the reliability improves, but the productivity deteriorates due to increased downtime
Solution Approach 1:
The patent applies feedback by continuously monitoring robot operation status, component health, and maintenance history, then using this information to optimize maintenance scheduling. The system receives feedback from sensors and operational data, adjusts maintenance timing based on actual component condition rather than fixed schedules, and refines workload adjustments based on observed performance patterns, achieving reliability optimization with minimal productivity impact.
Data Source
AI summary
A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.