Oil pipeline leak is a continuous and dynamic process affected by many factors (e.g., corrosion, wear, weld defects, vibration, erosion and manmade destruction). The method based on pressure signal detection and Gaussian assumption signal analysis cannot meet the characteristics of multivariable, strong coupling and dynamics. In this article, the operating and environmental parameters associated with the pipeline leaking are comprehensively considered. A novel oil pipeline leak detection method based on Dynamic Kernel Independent Component Analysis (DKICA) is proposed to solve the timingsequenceautocorrelation problem of the pipeline monitoring parameters and enhance the detection accuracy. Firstly, the optimal order of the model parameters is confirmed by the determination characteristics of dynamic (DOD) algorithm to reduce the autocorrelation among the monitoring parameters. Secondly, the Kernel Independent Component Analysis (KICA) is utilized to extract the independent component in kernel principal space. Finally, the pipeline leak is monitored by T2, SPE and the combined index of the independent components. Experimental results indicate that both the missing and false detection accuracies of the combined index D2 are much lower than those of the SPE and T2 separately. Additionally, both the missing and false detection accuracies of the 2order DKICA are much lower than those of KICA, due to the consideration of the dynamic characteristics. It verifies the feasibility and effectiveness of the proposed method based on DKICA for the oil pipeline leak detection.