The fault detection time of many methods based on kernel independent component analysis(KICA)is easily affected by the order of independent components(ICs)and the number of dominant ICs chosen empirically.Aiming at this problem

a method based on KICA and Gaussian mixture model(GMM)is proposed.KICA is used to extract ICs from the dataset measured under the normal condition

GMM is adopted to fit the probability density function of each IC

and then a monitoring statistic and corresponding control limit are constructed based on the GMM.The average value of each IC’s monitoring statistic is calculated and applied to judge its non-Gaussian degree.Each strong non-Gaussian IC is monitored independently by the built monitoring statistic

and the weak non-Gaussian part is monitored by principal component analysis.The simulation results on the Tennessee Eastman process illustrate that

in contrast to the fault detection methods based on KICA

the proposed method needn’t sort the ICs and select the number of dominant ICs avoiding their effects on fault detection time

make effective use of process information

and shorten the fault detection latency.