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In recent years, an increasing popularity of deep learning model forintelligent condition monitoring and diagnosis as well as prognostics used formechanical systems and structures has been observed. In the previous studies,however, a major assumption accepted by default, is that the training andtesting data are taking from same feature distribution. Unfortunately, thisassumption is mostly invalid in real application, resulting in a certain lackof applicability for the traditional diagnosis approaches. Inspired by the ideaof transfer learning that leverages the knowledge learnt from rich labeled datain source domain to facilitate diagnosing a new but similar target task, a newintelligent fault diagnosis framework, i.e., deep transfer network (DTN), whichgeneralizes deep learning model to domain adaptation scenario, is proposed inthis paper. By extending the marginal distribution adaptation (MDA) to jointdistribution adaptation (JDA), the proposed framework can exploit thediscrimination structures associated with the labeled data in source domain toadapt the conditional distribution of unlabeled target data, and thus guaranteea more accurate distribution matching. Extensive empirical evaluations on threefault datasets validate the applicability and practicability of DTN, whileachieving many state-of-the-art transfer results in terms of diverse operatingconditions, fault severities and fault types.

Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application