Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks
来源期刊：中南大学学报(英文版)2014年第6期
论文作者：Mousavi Hamidreza Shahbazian Mehdi Jazayeri-Rad Hooshang Nekounam Aliakbar
文章页码：2273 - 2281
Key words：fault diagnosis; nonlinear principal component analysis; auto-associative neural networks
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.
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
