文献类型：期刊文献

英文题名：Fault detection and diagnosis via standardized k nearest neighbor for multimode process

作者：Song, Bing[1];Tan, Shuai[1];Shi, Hongbo[1];Zhao, Bo[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2020

卷号：106

起止页码：1

外文期刊名：JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS

收录：;EI(收录号：20194807748650);WOS:【SCI-EXPANDED(收录号:WOS:000514014900001)】；

基金：This research is supported by the National Natural Science Foundation of China (nos. 61673173, 61703161), Fundamental Research Funds for the Central Universities (nos. 222201717006, 222201714031), National Natural Science Foundation of Shanghai (no. 19ZR1473200).

语种：英文

外文关键词：Multimode process; Fault detection; Fault diagnosis; Standardized distance; k nearest neighbor

摘要：For the multimode process, the scale information of every single mode never be considered in the distance calculation between the data and its neighbors in k nearest neighbor (kNN). This work proposes a standardized kNN (SkNN) based fault detection method, where a standardized distance is developed to characterize the distance between the data and its neighbors taking the scale information within mode and mode to mode into consideration. In addition, compared with the kNN based fault diagnosis method, the importance of various neighbors is considered through constructing the weights and giving to different neighbors in the SkNN based fault diagnosis method. Moreover, when there is more than one fault variable, in order to eliminate the influence of other fault variables on current reconstructed variable and reduce the computational complexity, concurrent reconstructed strategy and greedy algorithm are used in the SkNN based fault diagnosis method. At last, an industrial case study is employed to prove the effectiveness and advantage of the proposed SkNN based fault detection and diagnosis method. (C) 2019 Published by Elsevier B.V. on behalf of Taiwan Institute of Chemical Engineers.