CAJ | 학술논문

传统的多元统计过程控制(MSPC)的故障诊断方法要求观测变量数据服从高斯分布，然而实际化工流程中的仪表数据中难以满足这一要求。针对这一问题，提出在仪表数据中提取分离出非高斯信息和高斯信息，并分别利用独立元分析法和主元分析法建立不同的故障诊断模型。在检测到发生故障后，通过改进的贡献度算法定位出发生故障的仪表。通过对Tennessee Eastman(TE)过程数据进行仿真研究，验证了ICA-PCA故障诊断法在化工流程仪表不同故障诊断中的有效性。
전통적다원통계과정공제(MSPC)적고장진단방법요구관측변량수거복종고사분포，연이실제화공류정중적의표수거중난이만족저일요구。침대저일문제，제출재의표수거중제취분리출비고사신식화고사신식，병분별이용독립원분석법화주원분석법건립불동적고장진단모형。재검측도발생고장후，통과개진적공헌도산법정위출발생고장적의표。통과대Tennessee Eastman(TE)과정수거진행방진연구，험증료ICA-PCA고장진단법재화공류정의표불동고장진단중적유효성。
Multivariate statistical process control(MPSC) method assumes that the monitored variables have a Gaussian distribution. In fact, most of the instrument data don’t have a Gaussian distribution. A instrument fault monitoring method based on independent component analysis(ICA) and principal component analysis(PCA) is proposed to extract the Gaussian and non-Gaussian information for fault detection and diagnosis. The fault source of instrument can be determined by contribution algorithm. The proposed fault diagnosis method is proved to be effective by simulation with the data from the Tennessee Eastman process.