Industrial process fault detection plays an important role for process security, and data-based process monitoring is an effective way to detect fault. Industrial processes typically have multiple operating modes with different data distribution and outliers. This paper proposes a novel multimode process fault detection method based on local density ratio-weighted support vector data description (LDR-wSVDD) to address the multimodal process monitoring with different density and outliers in training samples. By considering both global density distribution and local data structure, this method provides a weight for each training sample based on its density information to make sure that the outliers with lower weight have less influence on the normal sample model. Meanwhile, it maintains the efficiency of SVDD single hypersphere model for multimode processes. The feasibility and validity of the LDR-wSVDD approach for multimode multidensity process monitoring are demonstrated through a numerical example and Tennessee Eastman benchmark process.