Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Saeed, Nagham; Abbod, Maysam F.

doi:10.5815/ijisa.2017.10.01

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Neuro-fuzzy can approximate certain types of nonlinear functions well in nature. Therefore, neuro-fuzzy models have been applied in designing control systems, such as the temperature control system for greenhouse [33], an antilock braking system of motor vehicle [34], a water-level control of Utube steam generators in nuclear power plants [35], and so on [3,7,8]. This study have exhibited that proposed methods have better properties than the conventional counter methods in function approximations and realworld benchmark problems.…”

Section: Discussionmentioning

confidence: 97%

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Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

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Abstract-The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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Section: Discussionmentioning

confidence: 97%

“…Conceiving complementary strengths of neural and fuzzy systems, neuro-fuzzes have been applied to handle numerous real-life problems including control, function approximations, classifications, etc. [1][2][3][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

View full text Add to dashboard Cite

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“…The research in [37] paper used a particle swarm algorithm as a clustering technique for its diagnostic methods and MATLAB software as the visualisation technique. Logistic regression models (statistical models based on traditional mathematical equation) and artificial intelligence (AI) [38] modelling are used as predictive methods alongside genetic algorithms (GA) [39] and neuro-fuzzy [40] techniques used for classification. Excel, and Simulink software were the visualisation techniques.…”

Section: Correlation Between Big Data Analytical Methods and Techniquesmentioning

confidence: 99%

Big Data Analytics Correlation Taxonomy

Husamaldin

Saeed

2019

Information

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Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.

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“…Distributed FDI architecture consists of subsystem models and is implemented with local diagnosers that together guarantee the same diagnosability as a regular centralized architecture, allowing high performance in scalability, reliability, communication, and reduced computation costs [27][28][29]. In this sense, given that oil pipelines are large-scale distributed plants [4,30] with many components, it would be more convenient to apply the distributed FDI approach, in which, unlike centralized approaches, knowing the model of the global plant is not mandatory [17,25,31]. Additionally, the main performance indexes of FDI methods in OPS are positioning accuracy, response time, sensitivity, and false alarm rate [32].…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Detection and Isolation Approach for Oil Pipelines

et al. 2021

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Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated.

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Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Cited by 5 publications

References 10 publications

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Big Data Analytics Correlation Taxonomy

Distributed Fault Detection and Isolation Approach for Oil Pipelines

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