文献类型：期刊文献

英文题名：Fault Detection and Identification Based on the Neighborhood Standardized Local Outlier Factor Method

作者：Ma, Hehe[1];Hu, Yi[1];Shi, Hongbo[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2013

卷号：52

期号：6

起止页码：2389

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20130816035758);WOS:【SCI-EXPANDED(收录号:WOS:000315080000024)】；

基金：This research was supported by the National Natural Science Foundation of China (No. 61074079) and Shanghai Leading Academic Discipline Project (No. B504).

语种：英文

外文关键词：Fault detection - Multivariant analysis - Process monitoring - Statistical process control

摘要：Complex chemical processes often have multiple operating modes to meet changes in production conditions. At the same time, the within-mode process data usually follow a complex combination of Gaussian and non-Gaussian distributions. The multimodality and the within-mode distribution uncertainty in multimode operating data make conventional multivariate statistical process monitoring (MSPM) methods unsuitable for practical complex processes. In this work, a novel method called neighborhood standardized local outlier factor (NSLOF) method is proposed. The local outlier factor of each sample, which means the degree of being an outlier, is used as a monitoring statistic. A new normalized Euclidean distance based on the local neighborhood standardization strategy is employed during the calculation of the monitoring index. Then, a contribution-based fault identification method is developed. Instead of building multiple monitoring models for complex chemical processes with different operating conditions, the proposed NSLOF method builds only one global model to monitor a multimode process without needing a priori process knowledge. Finally, the validity and effectiveness of the NSLOF approach are illustrated through a numerical example and the Tennessee Eastman process.