An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

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https://doi.org/10.1016/j.compind.2015.03.001
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Keywords

Data-driven control, Data mining, Evolving soft computing techniques, Fault diagnosis, Nature-inspired optimization algorithms, Wind turbines, PARTICLE SWARM OPTIMIZATION, GRAVITATIONAL SEARCH ALGORITHM, REDUCED PARAMETRIC SENSITIVITY, EXTREME LEARNING-MACHINE, TP MODEL TRANSFORMATION, FUZZY-LOGIC CONTROLLERS, PID-CONTROLLERS, CONTROL-SYSTEMS, SERVO SYSTEMS, EVOLUTIONARY ALGORITHMS

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An overview on fault diagnosis and nature-inspired optimal control of industrial process applications. / Precup, Radu-Emil; Angelov, Plamen ; Jales Costa, Bruno Sielly et al.
In: Computers in Industry, Vol. 74, 12.2015, p. 75-94.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{da9b3e4f4cad4cb7acbd6fb01830ec8e,

title = "An overview on fault diagnosis and nature-inspired optimal control of industrial process applications",

abstract = "Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.",

keywords = "Data-driven control, Data mining, Evolving soft computing techniques, Fault diagnosis, Nature-inspired optimization algorithms, Wind turbines, PARTICLE SWARM OPTIMIZATION, GRAVITATIONAL SEARCH ALGORITHM, REDUCED PARAMETRIC SENSITIVITY, EXTREME LEARNING-MACHINE, TP MODEL TRANSFORMATION, FUZZY-LOGIC CONTROLLERS, PID-CONTROLLERS, CONTROL-SYSTEMS, SERVO SYSTEMS, EVOLUTIONARY ALGORITHMS",

author = "Radu-Emil Precup and Plamen Angelov and {Jales Costa}, {Bruno Sielly} and Moamar Sayed-Mouchaweh",

year = "2015",

month = dec,

doi = "10.1016/j.compind.2015.03.001",

language = "English",

volume = "74",

pages = "75--94",

journal = "Computers in Industry",

issn = "0166-3615",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

AU - Precup, Radu-Emil

AU - Angelov, Plamen

AU - Jales Costa, Bruno Sielly

AU - Sayed-Mouchaweh, Moamar

PY - 2015/12

Y1 - 2015/12

N2 - Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.

AB - Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.

KW - Data-driven control

KW - Data mining

KW - Evolving soft computing techniques

KW - Fault diagnosis

KW - Nature-inspired optimization algorithms

KW - Wind turbines

KW - PARTICLE SWARM OPTIMIZATION

KW - GRAVITATIONAL SEARCH ALGORITHM

KW - REDUCED PARAMETRIC SENSITIVITY

KW - EXTREME LEARNING-MACHINE

KW - TP MODEL TRANSFORMATION

KW - FUZZY-LOGIC CONTROLLERS

KW - PID-CONTROLLERS

KW - CONTROL-SYSTEMS

KW - SERVO SYSTEMS

KW - EVOLUTIONARY ALGORITHMS

U2 - 10.1016/j.compind.2015.03.001

DO - 10.1016/j.compind.2015.03.001

M3 - Journal article

VL - 74

SP - 75

EP - 94

JO - Computers in Industry

JF - Computers in Industry

SN - 0166-3615

ER -

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