Corbinian Nentwich

• Technical University of Munich

Industrial robot gear condition monitoring has the potential to increase the productivity of highly automated production lines. In order to implement an effective condition monitoring system, data must be collected which correlates with the robot gear’s state of health. The sensor choice and the characteristics of these sensors are crucial to the s...

On the one hand, condition monitoring of industrial robot gears can reduce unexpected downtimes of highly automated production lines and thus save related costs. On the other hand, the implementation and operation of such systems is costly itself. In this context, we present a cost model to compare condition monitoring scenarios with preventive mai...

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 ano...

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 th...

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot...

Predictive Maintenance of industrial robots offers the potential to increase productivity and cut costs in highly automated production systems. The success of such maintenance strategies is highly dependent on the data acquisition strategy used to monitor the robot’s health state. In this publication, we first describe a methodology for deriving a...

Economic data acquisition and storage have been key enablers to pave the way for data-driven predictions of machine downtimes. Regarding industrial robots, such predictions can maximize the robot’s availability and effective life span. This paper focuses on the comparison of different data-driven models for robot fault prediction and classification...

Ungeplante Maschinenausfälle führen zu Stillständen in der Produktion, die große Auswirkungen auf die Wertschöpfungskette eines Unternehmens haben. Der Einsatz von Predictive Maintenance (PdM) entlang dieser Kette erhöht die Maschinenverfügbarkeit und sichert einen reibungslosen Produktionsablauf. Im Rahmen verschiedener Forschungsprojekte am iwb w...

Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well as the prediction of the product quality before t...