Complex chemical processes often have multiple operating modes to meet the changes of production conditions.Meanwhile

process complexity makes the within-mode data usually follow an uncertain combination of Gaussian and non-Gaussian distributions.The multimode characteristics and the uncertainty of data distribution within one single mode make the conventional multivariate statistical process monitoring(MSPM)methods unsuitable for fault detection.In this paper

a novel Mahalanobis distance-based local outlier factor(MDLOF)method was proposed.The local structure of variables was taken into account by employing Mahalanobis distances

and the local density of the surrounding neighborhoods was also considered.A density-based monitoring statistics which was robust regardless of data distribution was obtained by this way.Finally

the validity and effectiveness of MDLOF were illustrated through a numerical example and the Tennessee Eastman process.