Robust and Adaptive Non Destructive Testing of Hybrids with Guided Waves and Learning Agents

Priv.-Doz. Dr. Stefan Bosse
University of Bremen, Dept. Mathematics & Computer Science
University of Koblenz, Fac. Computer Science


Non-destructive Testing (NDT) of Structures And Structural Health Monitoring (SHM)

Big Data Challenges

  • Monitoring of mechanical structures is a Big Data challenge concerning Structural Health Monitoring and Non-destructive Testing

  • The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is characterized by a

    • High dimensionality in the temporal domain, and moreover
    • High dimensionality in the spatial domain using 2D scanning.

The quality of the results gathered from guided wave analysis depends strongly on the pre-processing of the raw sensor data and the selection of appropriate region of interest windows (ROI) for further processing (Feature Selection).


Some Definitions

Feature Selection
The task of feature selection separates relevant (information correlated) from irrelevant (information uncorrelated) data and performs the first significant data reduction of measuring sensor data.
  • Feature selection is traditionally hand made by experts, sometimes using regression or curve fitting
Feature Extraction
The task of feature extraction derives meaningful information from the already pre-selected input data.

  • Damages (class, localization, depth, ..)
  • Fatigue
  • Load changes
  • Lifetime prediction

Measurement and Analysis Challenges

Noise and Models

  • Noisy data makes feature selection difficult and unreliable

  • Usually complex material compositions and unknown damage models increase the unreliability of the feature selection and extraction task

Automatic Feature Selection

  • Adaptive and reliable input data reduction is required at the first layer of an automatic structural monitoring system.

  • Image segmentation can be used to identify ROIs as one relevant feature selection technique


Non-destructive Testing

  • NDT usually performs only a few point-to-point measurements to detect damages.

  • The two-dimensional recording of the wave propagation and interaction of guided waves can be performed by using laser vibrometry or an airborne ultrasonic testing technique Measuring time and big data are critical tasks

  • By adjusting the geometry of the actuator or its electrode configuration, the amplitudes of individual modes can be amplified or attenuated to emphasize specific wave interaction Parameter setting is critical task

  • The identification of damages is made by wave interactions, such as reflection, scattering, mode conversion and wave number changes, in wave propagation Feature Selection is critical task

  • A locally resolved scan of the wave propagation is required, producing wave propagation images with only a few regions of interest Segmentation is critical task

Automatic Monitoring System

  • Automated NDT system is proposed featuring:
    • Adaptive Segmentation & Feature Selection,
    • Machine Learning, Adaptive Filtering, and
    • SHM algorithms