Many fault detection methods based on kernel independent component analysis(KICA)only consider the extraction of non-Gaussian information

but the preservation of local neighborhood structure is usually ignored.Aiming at this problem

a fault detection method based on modified KICA is proposed.The criterion of negentropy maximization in KICA considering only the extraction of non-Gaussian information is converted to the criterion of entropy minimization.The criterion of entropy minimization is then combined with the criterion of similar local neighborhood structure in locality preserving projections(LPP)

and a new objective function taking both the extraction of non-Gaussian information and the preservation of local neighborhood structure into account is constructed.The particle swarm optimization algorithm(PSO)is utilized to optimize the objective function globally

and the monitoring statistics are built to monitor the process.The simulation results on Tennessee Eastman process illustrate that

in contrast to fault detection methods based on KICA

the proposed method can extract the non-Gaussian information of dataset while keeping the local neighborhood structure

shorten the fault detection latency and improve the fault detection rate effectively.