Speakers
Description
In Industry 4.0 factories, innovative prediction tools are adopted
so that data can be systematically processed into information that can
explain uncertainties and support decisions. Predictive manufacturing
systems begin with acquiring data from monitored assets using appropriate
sensors to extract various signals. These signals can then be integrated
with historical data into extensive datasets containing a multitude
of variables. Consequently, addressing the challenge of reducing
dimensionality becomes of paramount importance. Dimension reduction
techniques such as partial least squares (PLS) have recently gained attention
to deal with the problem of big datasets with a large number of
correlated variables. Standard PLS approaches confine the estimation to
examining only average effects, resulting in an insufficient portrayal. In
this paper, we combine the standard PLS technique with M-quantile regression.
The proposed approach aims at offering a more comprehensive
view of the effect of various dimensions on the degradation of etching
equipment in the microchip fabrication process.
| Type of presentation | Talk |
|---|---|
| Classification | Both methodology and application |
| Keywords | Partial Least Square, High Dimensional Data, Microelectronics |