  In the manufacturing process, sensor data collected from equipment is crucial
for building predictive models to manage processes and improve productivity.
However, in the field, it is challenging to gather sufficient data to build
robust models. This study proposes a novel predictive model based on the
Transformer, utilizing statistical feature embedding and window positional
encoding. Statistical features provide an effective representation of sensor
data, and the embedding enables the Transformer to learn both time- and
sensor-related information. Window positional encoding captures precise time
details from the feature embedding. The model's performance is evaluated in two
problems: fault detection and virtual metrology, showing superior results
compared to baseline models. This improvement is attributed to the efficient
use of parameters, which is particularly beneficial for sensor data that often
has limited sample sizes. The results support the model's applicability across
various manufacturing industries, demonstrating its potential for enhancing
process management and yield.
