文献类型：期刊文献

英文题名：Neighborhood preserving neural network for fault detection

作者：Zhao, Haitao[1,2];Lai, Zhihui[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Automat Dept, Shanghai 200237, Peoples R China;[2]Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China

年份：2019

卷号：109

起止页码：6

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20184406025121);WOS:【SCI-EXPANDED(收录号:WOS:000451027500003)】；

基金：This research is sponsored by the National Natural Science Foundation of China (61375007, 61573248), Basic Research Programs of Science and Technology Commission Foundation of Shanghai, China (15JC1400600), in part by the Natural Science Foundation of Guangdong Province, China (Grant 2017A030313367), and in part by the Shenzhen Municipal Science and Technology Innovation Council, China under Grant JCYJ20170302153434048.

语种：英文

外文关键词：Statistical process monitoring; Fault detection; Feedforward neural network; Neighborhood preserving embedding

摘要：A novel statistical feature extraction method, called the neighborhood preserving neural network (NPNN), is proposed in this paper. NPNN can be viewed as a nonlinear data-driven fault detection technique through preserving the local geometrical structure of normal process data. The "local geometrical structure ''means that each sample can be constructed as a linear combination of its neighbors. NPNN is characterized by adaptively training a nonlinear neural network which takes the local geometrical structure of the data into consideration. Moreover, in order to extract uncorrelated and faithful features, NPNN adopts orthogonal constraints in the objective function. Through backpropagation and eigen decomposition (ED) technique, NPNN is optimized to extract low-dimensional features from original high-dimensional process data. After nonlinear feature extraction, Hotelling T-2 statistic and the squared prediction error (SPE) statistic are utilized for the fault detection tasks. The advantages of the proposed NPNN method are demonstrated by both theoretical analysis and case studies on the Tennessee Eastman (TE) benchmark process. Extensive experimental results show the superiority of NPNN in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NPNN can be found in https://github.com/htzhaoecust/npnn. (c) 2018 Elsevier Ltd. All rights reserved.