  In recent years, an increasing popularity of deep learning model for
intelligent condition monitoring and diagnosis as well as prognostics used for
mechanical systems and structures has been observed. In the previous studies,
however, a major assumption accepted by default, is that the training and
testing data are taking from same feature distribution. Unfortunately, this
assumption is mostly invalid in real application, resulting in a certain lack
of applicability for the traditional diagnosis approaches. Inspired by the idea
of transfer learning that leverages the knowledge learnt from rich labeled data
in source domain to facilitate diagnosing a new but similar target task, a new
intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which
generalizes deep learning model to domain adaptation scenario, is proposed in
this paper. By extending the marginal distribution adaptation (MDA) to joint
distribution adaptation (JDA), the proposed framework can exploit the
discrimination structures associated with the labeled data in source domain to
adapt the conditional distribution of unlabeled target data, and thus guarantee
a more accurate distribution matching. Extensive empirical evaluations on three
fault datasets validate the applicability and practicability of DTN, while
achieving many state-of-the-art transfer results in terms of diverse operating
conditions, fault severities and fault types.
