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
qualityof 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).
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
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
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