  The failure of a complex and safety critical industrial asset can have
extremely high consequences. Close monitoring for early detection of abnormal
system conditions is therefore required. Data-driven solutions to this problem
have been limited for two reasons: First, safety critical assets are designed
and maintained to be highly reliable and faults are rare. Fault detection can
thus not be solved with supervised learning. Second, complex industrial systems
usually have long lifetime during which they face very different operating
conditions. In the early life of the system, the collected data is probably not
representative of future operating conditions, making it challenging to train a
robust model.
  In this paper, we propose a methodology to monitor the systems in their early
life. To do so, we enhance the training dataset with other units from a fleet,
for which longer observations are available. Since each unit has its own
specificity, we propose to extract features made independent of their origin by
three unsupervised feature alignment techniques. First, using a variational
encoder, we impose a shared probabilistic encoder/decoder for both units.
Second, we introduce a new loss designed to conserve inter-point spacial
relationships between the input and the learned features. Last, we propose to
train in an adversarial manner a discriminator on the origin of the features.
Once aligned, the features are fed to a one-class classifier to monitor the
health of the system. By exploring the different combinations of the proposed
alignment strategies, and by testing them on a real case study, a fleet
composed of 112 power plants operated in different geographical locations and
under very different operating regimes, we demonstrate that this alignment is
necessary and beneficial.
