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Authors: Haris M. Khalid 1 ; S. Z. Rizvi 1 ; Lahouari Cheded 1 ; Rajamani Doraiswami 2 and Ammar Khoukhi 1

Affiliations: 1 King Fahd Univ. of Petroleum & Minerals, Saudi Arabia ; 2 University of New Burnswick & National Science and Engineering Research Council (NSERC), Canada

ISBN: 978-989-8425-32-4

Keyword(s): Particle swarm optimization (PSO), Hybrid neuro-fuzzy, Soft computing, ANN, ANFIS, Fault detection, Benchmarked laboratory scale two-tank system.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neuro-Fuzzy Systems ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: When a fault occurs during an industrial inspection, workmen have to manually find the location and type of the fault in order to remove it. It is often difficult to accurately find the location and type of fault. Hence, development of an offline intelligent fault diagnosis system for process control industry is of great importance since successful detection of fault is a precursor to fault isolation using corrective actions. This paper presents a novel hybrid Particle Swarm Optimization (PSO) and Subtractive Clustering (SC) based Neuro-Fuzzy Inference System (ANFIS) designed for fault detection. The proposed model uses the PSO algorithm to find optimal parameters for (SC) based ANFIS training. The developed PSO-SC-ANFIS scheme provides critical information about the presence or absence of a fault. The proposed scheme is evaluated on a laboratory scale benchmark two-tank process. Leakage fault is detected and results are presented at the end of the paper showing successful diagnosis o f most incipient faults when subjected to a fresh set of data. (More)

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Paper citation in several formats:
M. Khalid, H.; Z. Rizvi, S.; Cheded, L.; Doraiswami, R. and Khoukhi, A. (2010). A PSO-TRAINED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FAULT CLASSIFICATION.In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 399-405. DOI: 10.5220/0003072303990405

@conference{icnc10,
author={Haris M. Khalid. and S. Z. Rizvi. and Lahouari Cheded. and Rajamani Doraiswami. and Ammar Khoukhi.},
title={A PSO-TRAINED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FAULT CLASSIFICATION},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={399-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003072303990405},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - A PSO-TRAINED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FAULT CLASSIFICATION
SN - 978-989-8425-32-4
AU - M. Khalid, H.
AU - Z. Rizvi, S.
AU - Cheded, L.
AU - Doraiswami, R.
AU - Khoukhi, A.
PY - 2010
SP - 399
EP - 405
DO - 10.5220/0003072303990405

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