Recently, domain adaptation has received extensive attention for solving intelligent fault diagnosis problems. It aims to reduce the distribution discrepancy between the source domain and target domain through learning domain-invariant features. However, most existing domain adaptation methods mainly focus on global domain adaptation and overlook subdomain adaptation, which results in the loss of fine-grained information and discriminative features. To address this problem, in this article, a deep adversarial subdomain adaptation network is proposed. This network aligns the relevant distributions of subdomains by minimizing the local maximum mean discrepancy loss of the same categories in the source domain and target domain. Under the constraints of global domain adaptation and subdomain adaptation, the distribution discrepancy is reduced from the domain and category levels. Four transfer tasks under different machine rotating speeds and six transfer tasks on different but related machines were used to evaluate the effectiveness of the proposed method. The results demonstrated the robustness and superiority of the proposed method over five other methods.