Robot Diagnostic System Using Multi-Segment Queueing for Proactive Maintenance
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Solution Overview
Problem
Modern manufacturing facilities face challenges in minimizing downtime and reducing maintenance costs due to unpredictable robot failures, as robots cannot communicate issues like failing bearings or encoders, leading to premature part replacement and production disruptions.
Innovation Solution
A system and method that collects real-time data from robots using a multi-segment queueing mechanism, analyzes it for anomalies, and generates reports to determine necessary maintenance and optimization, allowing for proactive scheduling and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regularly scheduled maintenance intervals are used, then maintenance can be performed systematically, but components may be replaced prematurely or robots may experience unexpected failures
Solution Approach 1:
The system performs preliminary actions by continuously monitoring robot operational data and predicting potential failures before they occur. The prognostic reporting mechanism analyzes trends in encoder performance, bearing temperatures, and other critical parameters to identify components that will fail within a predicted timeframe, allowing maintenance to be scheduled proactively rather than reactively or on fixed intervals.
Solution Approach 2:
The system implements feedback by continuously collecting real-time data from robots during operation and using this information to adjust maintenance scheduling. The monitoring system provides ongoing feedback on component health status, enabling dynamic adjustment of maintenance intervals based on actual condition rather than predetermined schedules, thus preventing both premature replacement and unexpected failures.
2Ease of repair
If replacement parts are ordered for unexpected failures, then robot repairs can be performed, but long lead times result in extended periods of inoperability
Solution Approach 1:
The system performs preliminary action by predicting component failures before they occur and automatically initiating parts ordering processes in advance. The prognostic reporting system identifies components that will fail within a predicted timeframe, allowing maintenance teams to order replacement parts before the actual failure occurs, thereby eliminating the long lead times associated with unexpected failures and ensuring parts are ready when maintenance is scheduled.
3Productivity
If robots operate under extreme conditions, then production output can be maximized, but components fail more frequently before scheduled maintenance
Solution Approach 1:
The system implements feedback by continuously monitoring robot operational data including temperatures, load conditions, and cycle counts during extreme operation. This real-time feedback allows the prognostic system to detect accelerated wear patterns and adjust maintenance schedules dynamically, enabling robots to operate at maximum productivity while preventing component failures through proactive maintenance scheduling based on actual condition monitoring.
4Loss of time
If spare parts are inventoried in-house, then replacement can be performed quickly, but inventory costs increase
Solution Approach 1:
The system performs preliminary action by predicting which parts will be needed based on failure probability analysis and automatically initiating procurement processes in advance. This allows the system to maintain lower inventory levels while ensuring critical parts are ordered and available before they are actually needed, reducing inventory costs while maintaining quick replacement capability for predicted failures.
Data Source
AI summary
A robot data transfer method includes the step of collecting data from each of a plurality of robots in a multi-robot production facility in real-time. The data collected from the robots is then transferred in real-time from a controller of each of the robots to a first data collection device. Within the first data collection device, the data is buffered using a multi-segment queueing mechanism. The queueing mechanism is configured with a retention policy. The data is then transferred to a second data collection device based on the retention policy of the queueing mechanism of the first data collection device. The second data collection device analyzes the data and determines whether maintenance or optimization is necessary for any of the robots.