自然科学版 英文版
自然科学版 英文版
自然科学版 英文版
自然科学版 英文版
英文版编委
自然科学版 英文版
英文版首届青年编委

您目前所在的位置:首页 - 期刊简介 - 详细页面

中南大学学报(英文版)

Journal of Central South University

Vol. 21    No. 6    June 2014

[PDF Download]    [Flash Online]

    

Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks
Mousavi Hamidreza1, Shahbazian Mehdi1, Jazayeri-Rad Hooshang1, Nekounam Aliakbar2

1. Department of Automation and Instrumentation Engineering,
Petroleum University of Technology, Ahwaz 63431, Iran;
2. Khuzestan Gas Company, Instrumentation Unit, Ahwaz 63428, Iran

Abstract:Fault diagnostics is an important research area including different techniques. Principal component analysis (PCA) is a linear technique which has been widely used. For nonlinear processes, however, the nonlinear principal component analysis (NLPCA) should be applied. In this work, NLPCA based on auto-associative neural network (AANN) was applied to model a chemical process using historical data. First, the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN (E-AANN) was presented to isolate and reconstruct the faulty sensor simultaneously. The proposed method was implemented on a continuous stirred tank heater (CSTH) and used to detect and isolate two types of faults (drift and offset) for a sensor. The results show that the proposed method can detect, isolate and reconstruct the occurred fault properly.

 

Key words: fault diagnosis; nonlinear principal component analysis; auto-associative neural networks

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
版权所有:《中南大学学报(自然科学版、英文版)》编辑部
地 址:湖南省长沙市中南大学 邮编: 410083
电 话: 0731-88879765(中) 88836963(英) 传真: 0731-88877727
电子邮箱:zngdxb@csu.edu.cn 湘ICP备09001153号