Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing

Abstract

Abstract In recent years, transfer learning has become more and more favored by scholars from all walks of life. At present, although transfer learning has achieved certain results in the field of fault diagnosis, the use of transfer learning alone may lead to poor transfer effects or even negative transfer due to the sample gap being under variable conditions in the same machinery. Therefore, deep domain adaptation with adversarial idea and coral alignment (DAACA) is proposed in this paper in order to solve the problem. DAACA is briefly summarized below. The domain adaptation with adversarial idea is added on the basis of transfer learning. The deep coral is then appended to further reduce the distribution difference between the data from the source and the target domain, which improves the invariant features of adversarial domain adaptation learning. In addition, a gradient reversal layer is introduced in the method to achieve gradient reversion and avoid the adversarial disadvantage of fixing parameters separately. It can be seen from the experimental results that the DAACA can not only solve the problem caused by the sample gap in variable conditions, but also achieve higher diagnosis accuracy and generalization ability.