Over time, robots degrade because of age and wear, leading to decreased reliability and increased potential for faults and failures. The effect of faults and failures impacts robot availability. Economic factors motivate facilities and factories to improve maintenance techniques and operations to monitor robot degradation and detect faults, especially to eliminate unexpected shutdowns. Since robot systems are complex, with sub-systems and components, it is challenging to determine these constituent elements' specific influences on the overall system performance. The development of monitoring, diagnostic, and prognostic (collectively known as Prognostics and Health Management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. This paper presents the strategy of integrating top level and component level PHM to detect robot performance degradation (including robot tool center accuracy degradation), supported by the development of four-level sensing and analysis structure. Top level PHM can quickly detect robot tool center accuracy degradation through the advanced sensing and test methods developed at NIST. PHM data from other levels are added for deep data analysis (for root cause diagnostics and prognostics). A reference data set is collected and analyzed using the integration of top level PHM and component PHM to understand the influences of robot tool center accuracy degradation from temperature, speed, and payload.
Proceedings Title: ASME International Manufacturing Science and Engineering Conference
Conference Dates: June 18-22, 2018
Conference Location: College Station, TX
Pub Type: Conferences
Condition Monitoring, Diagnostics, Prognostics Maintenance, Manufacturing Processes, Robot Systems, Robot Performance Degradation