To solve the problem of missing different contribution of each kernel independent component for the system fault by using the kernel independent component analysis fault detection method, a weighted kernel independent component analysis is proposed for fault detection. The kernel independent component analysis is performed to extract the kernel independent components. Kernel density estimation is used to evaluate the contribution of each kernel independent component. According to the contribution, different weighting values are set up to highlight the kernel independent components with more useful information. Finally, the local outlier factor is used to establish the monitoring statistic in the feature space. The advantages of the proposed method are demonstrated by the results based on a numerical and the Tennessee Eastman process simulation.