Incipient faults are difficult to detect in plant-wide processes compared to conventional faults due to the lack of distinctive features. An incipient fault detection method based on multiple subspace weighted moving window principal component analysis (PCA) was proposed by shifting the detection perspective from the global to the local to improve the detection rate and sensitivity of incipient faults in plant-wide processes. Process variables were partitioned into different subspaces using a two-layer subspace partitioning method that combines process knowledge and data-driven approaches. A weighted moving window was used to increase the offset of incipient faults, while a local outlier factor (LOF) algorithm was introduced into PCA to further focus on the local features of the data to model fault detection in each subspace. The monitoring results in each subspace were fused with information by the Bayesian inference fusion method to obtain distributed monitoring results. The proposed method was validated by industrial examples, and the results showed that the method effectively improved the accuracy and detection speed of incipient fault detection in plant-wide processes.