  The traditional paradigm for developing machine prognostics usually relies on
generalization from data acquired in experiments under controlled conditions
prior to deployment of the equipment. Detecting or predicting failures and
estimating machine health in this way assumes that future field data will have
a very similar distribution to the experiment data. However, many complex
machines operate under dynamic environmental conditions and are used in many
different ways. This makes collecting comprehensive data very challenging, and
the assumption that pre-deployment data and post-deployment data follow very
similar distributions is unlikely to hold. Transfer Learning (TL) refers to
methods for transferring knowledge learned in one setting (the source domain)
to another setting (the target domain). In this work, we present a TL method
for predicting Remaining Useful Life (RUL) of equipment, under the assumption
that labels are available only for the source domain and not the target domain.
This setting corresponds to generalizing from a limited number of
run-to-failure experiments performed prior to deployment into making
prognostics with data coming from deployed equipment that is being used under
multiple new operating conditions and experiencing previously unseen faults. We
employ a deviation detection method, Consensus Self-Organizing Models (COSMO),
to create transferable features for building the RUL regression model. These
features capture how different target equipment is in comparison to its peers.
The efficiency of the proposed TL method is demonstrated using the NASA
Turbofan Engine Degradation Simulation Data Set. Models using the COSMO
transferable features show better performance than other methods on predicting
RUL when the target domain is more complex than the source domain.
