Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions

Remaining useful life (RUL) prediction can effectively avoid unexpected mechanical breakdowns, thus improving operational reliability. However, the distribution discrepancy caused by different working conditions may lead to deterioration in the prognostic task of machinery. Inspired by the idea of transfer learning, a novel intelligent approach based on dynamic domain adaptation (DDA) is proposed for the machinery RUL prediction of multiple working conditions in this paper. At first, reverse validation technology is utilized to select appropriate source samples to construct the training dataset. Then two dynamic domain adaptation networks are trained to extract domain invariant degradation feature and predict RUL, namely dynamic distribution adaptation network and dynamic adversarial adaptation network. In the dynamic domain adaptation network, the fuzzy set theory is employed to calculate conditional distribution discrepancy loss, and the dynamic adaptive factor is introduced to dynamically adjust the distribution weights. Finally, the proposed method is proved to be effective through two run-to-failure bearing datasets. Related experimental results indicate that, compared with other related RUL prediction methods, the DDA-based prognostic method not only achieves better prediction performance, but also avoids the influence of negative transfer and distribution weight fluctuation.