In order to deal with the problem that locality preserving projections (LPP) does not take into account the global structure and dynamic characteristic of process data

a new fault detection method based on dynamic sparse locality preserving projections (DSLPP) is proposed. In the study

the original data matrix is firstly extended to a time-delay augmented matrix. Then

a sparse coefficient matrix which can represent global sparse reconstructive relationship of data is gotten by solving an optimal problem of sparse representation (SR). The sparse coefficient matrix combines with the objective function of LPP to form a new objective function for dimensionality reduction. The new dimensionality reduction algorithm can not only preserve the local neighbor structure of the original data space

but also have better effect in preserving the global sparse reconstructive relationship. At last

DSLPP-based T2 and Q statistics are constructed respectively in the feature space and residual space for fault detection.The simulation results of Tennessee Eastman process demonstrate that the proposed method detects faults more quickly and achieves lower fault missing alarm rate than the LPP method.