LIBRARY ı6ıul 0) - Cranfield University

Chawla [ref. 161] utilised statistical feature extraction together with a windowing
technique for monitoring gas metal arc welding transient data. The windowing technique
was based on acquiring data during fixed periods of time (windows) and introducing a
time interval between windows, thus allowing the capture and analysis of transient welding
data over a reasonable length of weld. Each window was considered as independent of the
others and a feature extraction was carried out by calculating several statistical
characteristics for each data window, as follows:
9 Mean value, W..:
R'mean Nw
where W, are the transient data samples and
Nw is the number of data samples acquired in a fixed time period (window).
" Standard Deviation, W d. -
Wrd
IN,
(W
)2
Wmean
-
NN-1
" Peak value, Wpk:
N,
Iwp;
WPk NP
(2.25)
(2.26)
(2.27)
where Wp; are the transient samples with values greater than W..,,,, and
Np is the number of samples with this characteristic acquired in a window of
data.
" Background value, Wbk:
Nb
Wbk N 6
(2.28)
where Wb; are the transient samples with values smaller than W,
,,, and
Nb is the number of samples with this characteristic acquired in a window of
data.
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