What are the main challenges of wireless sensors and how they can be met?

Predictive maintenance represents the systematic application of statistical and machine learning methods to uncover hidden patterns and structures from process and machine data and eventually derive state forecasts from them. The aim is to identify impending unwanted situations before they occur*. The basis for predictive maintenance is mostly sensor-based condition monitoring and the integration and logical linking to existing process data.

Condition monitoring & predictive maintenance PM represents one of the main topics of the CFG Smart Systems and Systems Integration in 2021, for which a team has been built. The members meet regularly to discuss the development in this field and initiate cooperation projects. We are glad to report on our third session on June 15, 2021, with a focus on wireless sensor systems.

Because wireless sensor systems are strongly emerging in Industrial IoT,  appropriate systems and framework conditions for implementation should be known and met. The session on June 15, 2021, with keynotes from Jutta Isopp and Johannes Lehrhofer intensively discussed the main challenges of the contactless sensor systems, such as problems of connectivity, level of security, energy (high energy consumption, insufficient battery life), interoperability, cost models, the complexity of new sensors integration. The session keynotes provided the participants with a detailed analysis of the main wireless sensors in praxis and showed ways towards appropriate systems and framework conditions for implementation.

The session brought us a step closer to making use of all the benefits of being wireless and understand the complex challenges in praxis.


We thank the speakers and the Condition Monitoring & Predictive Maintenance team members for their inputs and lively interaction!

Our online sessions will continue after the summer pause.

Topics of the next workshop:

  • How wireless transmission works. Fundamental problems, range, bandwidth, power consumption/consumption, how are these related. Problems in the real world (Johannes Horvath, Analog Devices)
  • Silicon Austria Labs approaches in the field of condition monitoring & predictive maintenance (Wolfgang Muehleisen, Silicon Austria Labs)



Members of the Condition monitoring & predictive maintenance PM team of  CFG Smart Systems and Systems Integration: 

CISC Semiconductor GmbH, MessfeldLam ResearchMaterials Center Leoben Forschung GmbHLinz Center of Mechatronics GmbH , TechMeetsLegalFH CAMPUS 02JOANNEUM RESEARCH Forschungsgesellschaft mbHAnalog DevicesK3lab, Providens AnalyticsSilicon Austria LabsUniversität Klagenfurt

(* Source: https://f.hubspotusercontent00.net/hubfs/4148856/usu_infocenter/ai/ai_wp_predictive-maintenance/usu_wp_predictive_maintenance_de_web.pdf?__hstc=&__hssc=&hsCtaTracking=6514c3e1-2af8-4f66-817a-855c6192e915%7Ce1dcf0be-abbb-4c37-968d-150d301b2d80),.



We are happy to get in touch with new interested colleagues on the matters of condition monitoring and predictive maintenance!