Bauhaus Luftfahrt Jahrbuch 2019
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29
Flussdiagramm des
ML-Prozesses
Das ML-Modell wird darauf trainiert, akustische
Merkmale verschiedener Prozessqualitäten zu
unterscheiden. Aus dieser Klassifikationsfähigkeit
wurde die Detektionswahrscheinlichkeit für
mehrere Defektgrößen abgeleitet.
AE-sensing for varying
processing quality
Data acquisition &
signal conditioning
Data collection
Time-domain signal
Flow chart of ML process
The ML model is trained to differentiate
between acoustic features of different process
qualities. The probability of detection for
several defect sizes was derived from this
classification capability.
Time-frequency spectrum
Training data set
Feature extraction
Testing data set
Data transformation &
feature extraction
ML algorithm training
& testing
Training feature classification
Output trained ML model
Evaluation of classification
performance
Estimate of probability of
detection
ML algorithm
evaluation
Detektionswahrscheinlichkeit für variierende Defektgrößen
Das Potenzial, Defekte in-situ mittels Akustischer Emission (AE) zuverlässiger zu erkennen als mit etablierter
ex-/in-situ Computer-/Optischer Tomographie (CT/OT), könnte seitens Sensorik, ML und Abstufung untersuchter
Qualitätslevel noch erhöht werden.
Probability of detection for
varying defect sizes
The potential of detecting defects in-situ more reliably
by using acoustic emission (AE) rather than established
ex-/in-situ computer/optical tomography (CT/OT) could
be increased on the part of sensors, ML, and gradation
of the examined quality level.
Probability of detection
1.0
0.8
0.6
0.4
0.2
0.0
0.05 0.10 0.15 0.20 0.25 0.30 0.35
Defect size [mm]
AE
AE 95 % Confidence Interval (C.I.)
CT
CT 95 % C.I.
OT
OT 95 % C.I.