The industrial world is in constant demand for faster, cheaper and higher quality manufacturing. Robot utilisation and automation has evolved to become a necessary asset to master in order to stay competitive in the global market. With the growing dependency on robots, unexpected downtime and brakedowns can cause devastating loss of revenue. Consequently, this has lead to an increased importance for an accurate condition based way of performing robotic maintenance. As of writing, robots are predominantly maintained through time dependent maintenance. Part replacement is based on statistical models where maintenance is performed without taking the actual robot condition into consideration. As a result an overall level of uncertainty is ensued, where lacking the ability to properly diagnose the robot, also leads to superfluous repairs. Because of the costly impact this has on production, a condition based maintenance approach to robots would yield increased reliability at a lower cost of maintenance. This research focuses on trying to monitor vibrations in a robot, so as to infer about wear and to provide a first step in vibration based Robot Condition Monitoring. This research has been of multidisciplinary nature where robotics, tribology, mechanical component, signal analysis and diagnosis theory have overlapped in several areas throughout the project. The research has provided a vibration baseline and trends of the theoretical bearing defect frequencies for a hypocycloid gearbox installed on an ABB IRB6600 robot. The gearbox was not worn to a level that a severe gearbox degradation was irrefutably detectable and analysable. Accelerometers normally used on wind turbines were used for the project, and are believed to be sufficiently successful in capturing bearing related signals to accredit it for continued use at the preliminary stages of Robot Condition Monitoring development. A worn RV410F hypocycloid gearbox, was dismantled and analysed. Bearings found inside indicate high degrees of moisture corrosion and extensive surface wear. These findings had decisive roles in what future work recommendations where presented. Areas with great potential are condition monitoring through the use of Acoustic Emission and lubrication analysis. Further recommendations include investigating signal analysis techniques such as cepstrum pre-whitening and discrete wavelet transforms.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-66011 |
| Date | January 2017 |
| Creators | Danielson, Hugo, von Schmuck, Benjamin |
| Publisher | Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Thesis: Master of Science in Mechanichal Engineering, |
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