Advanced NDT

In the scope of Industry 4.0-related testing, a huge volume of process data is accumulated from different sources and must be analyzed. Suitable methods can be applied to obtain further information from already determined or additionally measured parameters. Fraunhofer IKTS optimizes established and new NDT methods to show customers how they can use this valuable information.

Pattern recognition

With pattern recognition, test objects can be classified by means of their measurement signals, for example, through actively or passively acquired acoustic signals, images, or other parameters such as temperature values. The main focus of pattern recognition is on giving these data a meaning, for example, “The gear is defect-free” or “The valve has reached 80 % of its lifetime”.

Fraunhofer IKTS has a wealth of experience in the field of pattern recognition. Developed algorithms have already been successfully tested and used in numerous applications, e.g., in mechanical engineering, automotive, glass, paper, textile, and watch- and clockmaking industries. In addition to a PC-based solution, an autonomous, modular device for mobile measurements has been developed for connection of various sensors or microphones.


Technical details

  • Independent of testing method as well as sensor measurement principle and type
  • Combination of different sensor data possible


Application fields

  • OK/NOK analysis
  • Service life prediction
  • Detection of cracks, inclusions, and impact damage
  • Wear monitoring
  • Condition monitoring of parts, machines, and plants
  • Monitoring of production processes
© Fraunhofer IKTS
Hardware module for pattern recognition.
© Shutterstock / Zapp2Photo
Robot-assisted measurement and AI-based data analysis extend the range of applications for NDT methods.

Machine learning

Machine learning, as a subarea of artificial intelligence, is the process of learning from an existing, usually large set of data. This process does not occur by “rote learning”, but by recognition of patterns and regularities in known examples, the training data. In the training process, generalized models are built and can be used to classify new, unobserved data.

Fraunhofer IKTS uses special machine learning processes such as deep learning for training of deep neural networks (DNNs), the expectation maximization (EM) algorithm for hidden Markov models (HMMs), or convex optimization for support vector machines (SVMs). Special training software enables easy learning of new models, e.g., for additional series of the same part or comparable parts.


Services offered

  • Recognition and training software
  • Hardware modules
  • Data analysis and evaluation
  • Customer-specific development of complete systems

NDT assistance systems

Another focus of Industry 4.0 is on assistance systems intended to support humans in dealing with technology. Fraunhofer IKTS is developing a cognitive user interface for the control of testing systems. This cognitive UI enables a natural dialog between the user and the testing system. Thus, the user does not have to have any prior knowledge or learn any special commands. The interface independently adapts to the tester’s way of working and the testing tasks. It also learns the individual user’s behavior and can be controlled via various communication options (e.g., voice, touch, and gestures). This can, for instance, help testers operate test equipment when access to the test specimen is impeded or environmental conditions pose a complication (e.g., radioactively contaminated surroundings). The cognitive user interface developed at Fraunhofer IKTS has the advantage of being autonomous. It requires neither an Internet connection nor a radio network. In addition, the hardware module does not use any resources from the test equipment. For absolute data security, data are only kept on the device and are, by default, not transferred to external servers or clouds from third-party providers. This also makes the interface suitable for confidential and local applications involving sensitive data.

Technical details

  • Can be used without radio network or Internet connection
  • No transmission of user speech input to third-party servers
  • Enables non-contact “hands and eyes free” communications


Application fields


Maintenance, repair and operation (MRO) in:

  • Large-scale technical infrastructures
  • Aerospace
  • Industrial and plant engineering
  • Human-machine interaction
  • Control of equipment and plants


Services offered

  • Recognition software
  • Hardware
  • Training software
  • Customer-specific development