In order to solve the timing sequence and multistage problems in industrial production process, a fault detection method based on orthogonal local preserving projection(OLPP) and adaptive timing sequence window weighted k nearest neighbor (ATSWKNN) was proposed. Firstly, basing on maintaining the sample nearest neighbor relationship, the original data are projected into the lowdimensional feature space by using OLPP. Secondly, a certain kind of timing window is selected in the feature space, and the timing sequence square distance is calculated. Then, the reciprocal of the average cumulative square distance between the sample in the window and its spatial nearest neighbor set is taken as the weight. Finally, statistics are constructed to monitor the process. OLPPATSWKNN reduces the autocorrelation of process and solve the problem of multistage statistical difference by extracting time series information and introducing weight within the window. In addition, the problem of abnormal statistical indicators during phase switching is solved by adaptive window switching strategy. The monitoring performance of OLPPATSWKNN is verified by monitoring the numerical simulation process and penicillin fermentation process, and the monitoring results are significantly better than the classical methods.