关键词: 故障检测, 核独立元分析, 高斯混合模型, 独立元顺序, 主元分析法

Abstract: 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.

Key words: fault detection, kernel independent component analysis, Gaussian mixture model, order of independent components, principal component analysis