Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS

Lau, C.K. and Ghosh, K. and Hussain, M.A. and Hassan, C.R.C. (2013) Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemometrics and Intelligent Laboratory Systems, 120. pp. 1-14. ISSN 0169-7439

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Abstract

Fault diagnosis in industrial processes are challenging tasks that demand effective and timely decision making procedures under the extreme conditions of noisy measurements, highly interrelated data, large number of inputs and complex interaction between the symptoms and faults. The purpose of this study is to develop an online fault diagnosis framework for a dynamical process incorporating multi-scale principal component analysis (MSPCA) for feature extraction and adaptive neuro-fuzzy inference system (ANFIS) for learning the fault-symptom correlation from the process historical data. The features extracted from raw measured data sets using MSPCA are partitioned into score space and residual space which are then fed into multiple ANFIS classifiers in order to diagnose different faults. This data-driven based method extracts fault-symptom correlation from the data eliminating the use of process model. The use of multiple ANFIS classifiers for fault diagnosis with each dedicated to one specific fault, reduces the computational load and provides an expandable framework to incorporate new fault identified in the process. Also, the use of MSPCA enables the detection of small changes occurring in the measured variables and the proficiency of the system is improved by monitoring the subspace which is most sensitive to the faults. The proposed MSPCA-ANFIS based framework is tested on the Tennessee Eastman (TE) process and results for the selected fault cases, particularly those which exhibit highly non-linear characteristics, show improvement over the conventional multivariate PCA as well as the conventional PCA-ANFIS based methods.

Item Type: Article
Additional Information: 077GX Times Cited:0 Cited References Count:43
Uncontrolled Keywords: ANFIS, Feature extraction, MSPCA, Online fault diagnosis, Tennessee Eastman process, adaptive neurofuzzy inference system, article, computer aided design, conceptual framework, controlled study, correlation analysis, data analysis, data extraction, diagnostic error, fuzzy system, good laboratory practice, mathematical computing, multi scale principal component analysis, online system, principal component analysis, priority journal, process development, process model, process monitoring, scoring system, sensitivity analysis.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 00:53
Last Modified: 10 Jul 2013 00:53
URI: http://eprints.um.edu.my/id/eprint/6979

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