Research Topics

Industrial Data Analysis

Industrielle Datenanalyse
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The increasing digitalization and networking of production processes in the context of Industry 4.0 is leading to numerous challenges that demand innovative solutions. A particular focus lies here on the automatic data analysis of measurement and process data for intelligent condition monitoring and quality assurance.

Diverse data is collected already at many companies, however, this data is generally not extensively utilized. With efficient data structuring, self-learning systems and new big data approaches, the potential of this data can be exploited and converted into strategic competitive advantages.

For example, predictive approaches supply meaningful information about the condition of wear on components like motors, pumps and bearings. You, too, can profit from our innovative analysis algorithms and in-depth practical experience. We offer you an extensive portfolio ranging from condition monitoring to predictive maintenance and even quality assurance aspects in production processes.

Our Services

We provide you with individual advice and support in the optimization of your system availability and quality monitoring and offer you:

  • Feasibility studies on the use of condition monitoring systems
  • Analysis, classification and visualization of existing measurement and process data
  • Self-learning feature and boundary detection
  • Trend analysis
  • Distributed data analysis
  • Development of customer-specific stand-alone and embedded software
  • Dedicated algorithm development
  • Implementation of individual cloud solutions

Added Value for You

Our work based on the design and development of new types of automatic analysis algorithms will help you profit from the following effects:

An inexpensive and userfriendly CMS is especially important for electrical motors used in industrial plant systems.
© industrieblick / Fotolia.com

  • Interdisciplinary data analysis for product development, quality assurance and predictive maintenance
  • Seamless integration of big data and cloud infrastructures
  • Reduction of downtimes
  • Increased productivity and system availability
  • Cost savings through improved planning of repairs
  • Generation of strategic competitive advantages

References and more Information

Reference project

ACME 4.0

The partners are developing a highly integrated, easy-to-use, flexible and affordable platform with autonomous energy supply for acoustic monitoring in industrial applications. The aim is to utilize airborne sound and ultrasound for multi-faceted tasks.

Reference project

RAdCoM

The project partners worked on a functional prototype of a universally applicable system that uses various sensors for monitoring of structures either continuously or at regular intervals.