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
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
of most incipient faults when subjected to a fresh set of data.
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