文献类型：期刊文献

英文题名：Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

作者：Ran, Qilan[1];Song, Yedong[2];Du, Wenli[1];Du, Wei[1];Peng, Xin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Weichai Power Co Ltd, Weifang 261061, Peoples R China

年份：2021

卷号：9

期号：2

外文期刊名：PROCESSES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000623120800001)】；

基金：This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101), the National Natural Science Fund for Distinguished Young Scholars (61725301), International (Regional) Cooperation and Exchange Project (61720106008) and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：diesel engine; fault detection; canonical correlation analysis; variable selection; data-driven

摘要：In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach.