In order to solve the problem of poor classification performance and low identification accuracy of fault caused by few bearing fault samples, discrepancy in data distribution between source domain and target domain, and the lack of good cross domain feature representation, a domain adaptive intelligent fault diagnosis method for bearings based on transfer component analysis(TCA) was proposed. A new feature representation was established, a parametric kernel obtained by feature extraction method was used to perform domain adaption. The data were projected onto learned transfer components, and the maximum mean difference between the source domain and the target domain samples in the feature subspace was minimized. Then a reduced dimension feature subspace was obtained. Thus, the distance between domain distributions was significantly reduced, feature information transfer was constructed from source domain to target domain. Finally, the effectiveness of the proposed fault diagnosis method was verified by experiments. The results indicate that the highest classification accuracy of the proposed method can reach 95%, the average test accuracy can reach 81 %, which is about 70% higher than the accuracy of the common classification methods. The proposed algorithm can reduce the influence of domain distribution discrepancy and label noise, classify small sample data correctly and effectively. The health status of rolling bearing can be detected by the proposed methods. [ABSTRACT FROM AUTHOR]