Abstract
Investigation on the arc sound has been made to find the features which can represent the welding penetration. We first acquired original features of the signal by using wavelet packet decomposition. By the Branch and Bound (BB) method, we reduced the number of features and finally obtained a combination of features with certain representation. The result suggests that energy characteristics in some frequency ranges might be effective features to describe the acoustic signal which can be used for predicting welding penetrations.
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Ye, Z., Wang, J., Chen, S. (2011). Feature Selection of Arc Acoustic Signals Used for Penetration Monitoring. In: Tarn, TJ., Chen, SB., Fang, G. (eds) Robotic Welding, Intelligence and Automation. Lecture Notes in Electrical Engineering, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19959-2_25
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DOI: https://doi.org/10.1007/978-3-642-19959-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19958-5
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