Dr. Magnus Christian Proebster
Engineering Sensors Inertial Acceleration, Robert Bosch GmbH
Dr.-Ing. Magnus Proebster is a system architect in the domain Industry 4.0 at Robert Bosch GmbH. He attained the Dipl.-Ing. and Dr.-Ing. from the University of Stuttgart in 2008 and 2016, respectively.
His research topics focus on communication networks and protocols, especially in the area of wireless cellular networks. Furthermore, research and development interests include embedded systems, programming and system modeling.
At Bosch, he is a member of the department Engineering Sensors Inertial Acceleration.
He is currently working on the development of intelligent and autonomous sensor modules based on MEMS sensors. This comprises most importantly the selection of components, internal and external communication interfaces and protocols as well as the definition of requirements regarding software functionality and power management.
To this end, the anticipation of requirements depending on use case specific scenarios needs to be considered carefully in order to create a flexible and efficient sensor module to be integrated into a complete IoT ecosystem.
Application of MEMS Sensors for Condition and Process Monitoring
Condition monitoring is an important field allowing to achieve high process efficiency and machine availability, e.g. by so-called predictive maintenance. This means that unplanned crashes of important components can be avoided or maintenance intervals can be adapted to the true wear of components. Furthermore, process monitoring allows for early warnings and maximum transparency, e.g. in production and logistics. Leveraging the potential of MEMS sensors, both fields facilitate great steps forward in terms of efficiency and competitiveness.
Nowadays, many condition monitoring sensors applied in industrial production environments are analogue sensor solutions, which are very expensive and bulky compared to integrated MEMS-sensor devices. For example, a MEMS acceleration sensor may be as small as 2x2mm² and can be integrated into an embedded sensor device. The cost advantage of such IoT devices enables a more wide-spread sensor application, leading to a greater data base of measurement signals. With this, Big-Data analyses make it possible to perform predictive maintenance, identify efficiency bottlenecks and increase transparency and traceability for production quality.
Autonomous, intelligent and connected sensor devices make it easy to retrofit even old machines and link them to an Industry 4.0 eco-system in a cost-effective manner. Embedded microcontrollers can perform preprocessing and filtering of the relevant data in order to keep network and server loads at a feasible level or even to allow for stand-alone use cases, where the sensor device directly indicates a result or necessary action to the operator.