Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently

a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However

thesemethods omit the local information

which is also important for process monitoring and fault diagnosis. In this paper

a local and global statistics pattern analysis (LGSPA) method

which integrates SPA framework and locality preserving projections within the PCA

is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection

two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables

an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics

a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.