  In the current era, an increasing number of machine learning models is
generated for the automation of industrial processes. To that end, machine
learning models are trained using historical data of each single asset leading
to the development of asset-based models. To elevate machine learning models to
a higher level of learning capability, domain adaptation has opened the door
for extracting relevant patterns from several assets combined together. In this
research we are focusing on translating the specific asset-based historical
data (source domain) into data corresponding to one reference asset (target
domain), leading to the creation of a multi-assets global dataset required for
training domain invariant generic machine learning models. This research is
conducted to apply domain adaptation to the ironmaking industry, and
particularly for the creation of a domain invariant dataset by gathering data
from different blast furnaces. The blast furnace data is characterized by
multivariate time series. Domain adaptation for multivariate time series data
hasn't been covered extensively in the literature. We propose MTS-CycleGAN, an
algorithm for Multivariate Time Series data based on CycleGAN. To the best of
our knowledge, this is the first time CycleGAN is applied on multivariate time
series data. Our contribution is the integration in the CycleGAN architecture
of a Long Short-Term Memory (LSTM)-based AutoEncoder (AE) for the generator and
a stacked LSTM-based discriminator, together with dedicated extended features
extraction mechanisms. MTS-CycleGAN is validated using two artificial datasets
embedding the complex temporal relations between variables reflecting the blast
furnace process. MTS-CycleGAN is successfully learning the mapping between both
artificial multivariate time series datasets, allowing an efficient translation
from a source to a target artificial blast furnace dataset.

Machine Learning;Machine Learning