Extraction of surface image features for weardetection on ball screw drive spindles
Tobias Schlagenhauf, Max Heinzler, Jürgen Fleischer
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
Part of
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
Failures of production machines are often caused
by wear and the resulting failure of components. Therefore,
condition-based monitoring of machines and their components
is becoming an increasingly important factor in industry.
Due to the simple conversion of the motion of electric
rotary drives into precision feed motion, the ball screw
is an inherent element of many production machines. Thus,
a failure of the ball screw often leads to costly production
stops. This paper shows the determination and extraction
of wear-describing image features, allowing an image-based
condition monitoring of ball screws using hyperparameteroptimized
machine learning classifiers. The features to train
the algorithms are derived and extracted based on the deep
domain knowledge of ball screw drive failures in combination
with further developed state of the art feature extraction
algorithms.
Schlagenhauf, T et al. 2020. Extraction of surface image features for weardetection on ball screw drive spindles. In: Heizmann M. & Längle T (eds.), Forum Bildverarbeitung 2020. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000124383-24
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Veröffentlicht am 25. November 2020