To solve the problem of serial correlation of industrial data and the changes of global and local structure of data in some abnormal states,the "time lag migration" method was used to incorporate dynamic behavior into the multi-manifold projections(MMP) model,and an application scheme of dynamic multi-manifold projections(DMMP) algorithm in statistical process monitoring was proposed.Firstly,time-lag variables were added to the original sample data to make it dynamic.Secondly,the global and local structure information was obtained by solving the global graph maximum and local graph minimum separately.A unified framework,i.e.global graph maximum and local graph minimum,was constructed to extract meaningful low-dimensional representations for high-dimensional dynamic data.Finally,the fault detection was performed by comparing the statistics with control limits.The feasibility and effectiveness of the monitoring scheme based on DMMP was verified by the Tennessee-Eastman process.The simulation results show that the overall performance of DMMP is better than those of some traditional preserving global or local feature algorithms.The new algorithm solves the problem of incomplete acquisition of time-dependent data information in traditional algorithms,and provides a reference for improving the performance of traditional algorithms in fault detection of dynamic industrial process.