Search

Condition monitoring has the potential to increase the operating time and availability of industrial robot gears. In this dissertation, a reference architecture for such a condition-monitoring system is designed based on different datasets of accelerated wear tests. The reference architecture allows the automated detection of different defects of robot gearboxes and has the potential to make the development process of a condition-monitoring system for industrial robot gearboxes more efficient. Condition Monitoring (CM) besitzt das Potential die Verfügbarkeit von Industrierobotergetrieben zu erhöhen. Im Rahmen dieser Dissertation wird eine Referenzarchitektur für ein solches CM-System entworfen und anhand verschiedener Datensätzen von beschleunigten Verschleißtests evaluiert. Die Referenzarchitektur ermöglicht die automatisierte Detektion von Schäden bei Industrierobotergetrieben und besitzt das Potential die Entwicklung eines (CM)-Systems für Robotergetriebe effizienter zu gestalten.

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.