The domain adaptation problem has been widely studiedin the field of mechanical equipment fault diagnosis. At present, the most closed set domain adaptation methods generally assume the source domain and target domain share the same label space,which is not practical in real application. This can be called open set domain, because novel fault classes may actually emerge, these conventional methods which only rely on marginal distribution alignment are difficult to separate the new emerging classes and known classes. One of the typical open set domain adaptation problems isthat the label spaces of source domain and target domain are partially overlapped. In this article, a novel open set domain adaptation method based on adversarial dual classifiers(OSDA-ADC)is proposed to address this issue. Two neural networks with the same structure are introduced for adversarial training to enhance the domain adaptive performance of the model for known classes identification. The maximization and minimization entropy strategies of source domain and target domain, as well as the binary cross scheme of target domairl sample output are used to establish a boundary to isolate unknown classes. In addition, the bearing data set and the self-priming centrifugal pump are selected to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method can more accurately identify the existing known fault classes and new emerging unknown fault classes of mechanical equipment than the typical closed set domain and open set domain models. In each diagnosis task,the proposed method can achieve more than 90% accuracy.