In order to improve reliability of the excavator’s hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator’s hydraulic system. In order to improve reliability of the excavator’s hydraulic system, a fault detection approach based on dynamic principal component analysis (PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal, samples used as training data to develop a dynamic PCA model in the first step. secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace (PCS) and residual subspace The results show that the dynamic PCA model developed is able to detect the overall faults by using T2 statistic and Q statistic. By simulations analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively appli ed to the excavator’s hydraulic system.