Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines

Applications of deep transfer learning to intelligent fault diagnosis of machines commonly assume symmetry among domains: 1) the samples from target machines are balanced across all health states, and 2) the diagnostic knowledge required by target machines is consistent with source machines. In reality, however, such assumptions cannot be justified as machines operate normally in most of the time with only occasional faults. As a result, the collected monitoring data from target machines contain massive healthy samples but a small number of faulty samples, and some health states experienced in source machines may never happen in target machines. Therefore, if sufficient labeled data are available with diverse health states from the source machines, only partial diagnostic knowledge can be transferred to a target machine in presence of domain asymmetry. In order to selectively transfer diagnostic knowledge across asymmetric domains, we propose an adversarial network architecture named deep partial transfer learning network (DPTL-Net). The DPTL-Net uses a domain discriminator to automatically learn domain-asymmetry factors, by which the source machine samples are weighted to block irrelevant knowledge in the maximum mean discrepancy based distribution adaptation. The performance of the DPTL-Net is demonstrated in two case studies, where the diagnostic knowledge is transferred across different working conditions of a planet gearbox and across different yet related bearings. The results show that the DPTL-Net achieves better diagnostic performance than other transfer learning methods due to its transfer capability in presence of domain asymmetry.