  In this paper, a new approach is proposed for designing transferable soft
sensors. Soft sensing is one of the significant applications of data-driven
methods in the condition monitoring of plants. While hard sensors can be easily
used in various plants, soft sensors are confined to the specific plant they
are designed for and cannot be used in a new plant or even used in some new
working conditions in the same plant. In this paper, a solution is proposed for
this underlying obstacle in data-driven condition monitoring systems.
Data-driven methods suffer from the fact that the distribution of the data by
which the models are constructed may not be the same as the distribution of the
data to which the model will be applied. This ultimately leads to the decline
of models accuracy. We proposed a new transfer learning (TL) based regression
method, called Domain Adversarial Neural Network Regression (DANN-R), and
employed it for designing transferable soft sensors. We used data collected
from the SCADA system of an industrial power plant to comprehensively
investigate the functionality of the proposed method. The result reveals that
the proposed transferable soft sensor can successfully adapt to new plants.

Signal Processing;Machine Learning;Systems and Control;Systems and Control