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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Ameni El Anes Imen Ben Abdelwahed Kais Bouzrara Research Laboratory of Automatic Signal and Image Processing (LARATSI) National Engineering School of Monastir (ENIM) University of Monastir Rue Ibn El Jazzar Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

this paper presents a new PID control method for MIMO coupled systems based on the MIMO-ARX Laguerre model. This latter is obtained by expanding each polynomial function of the classical ARX MIMO model associated to the inputs and the outputs on independent Laguerre bases. The characteristic pole of which is estimated by minimizing the mean square error, where the gradient algorithm is applied. Numerical simulations illustrate the effectiveness of the proposed control technique.

关键词： Linear systems Adaptation models PI control Mean square error methods MISO communication Numerical simulation Polynomials

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of Engineering and Applied Science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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On the use of DPCA in process fault detection and identification

On the use of DPCA in process fault detection and identifica...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Mohamed Guerfel Hassani Messaoud Electronics Department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing (LARATSI) National Engineering School of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This paper proposes an innovative Dynamic Principal Component Analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is proposed to fix the data lag and the number of principal components to retain. A new structuration method is introduced to identify the process fault. It is based on the computation of common angles between residual subspaces of different modes. This structuration yields a concise set of detection indices, specifically sensitive to certain process faults while remaining unsensitive to others. The proposed FDI scheme is successfully applied to a three tank affected by several process faults.

关键词： Fault diagnosis Automation Fault detection Computational modeling Systems modeling Data models Principal component analysis

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Maintenance dynamic scheduling for a Tunisian railway transport system

Maintenance dynamic scheduling for a Tunisian railway transp...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Sarra Mellouli Anis Mhalla Simon Collart Dutilleul Hassani Messaoud Laboratory of Automatic Signal and Image Processing LARATSI National Engineering School of Monastir (ENIM) Monastir Tunisia Laboratory of Automation Electrical Systems Environment LAESE National Engineering School of Monastir (ENIM) Monastir Tunisia COSYS Department ESTAS Laboratory University Gustave Eiffel Lille France

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The developments reported in this paper are devoted to the dynamic scheduling of rail transport systems. In this context, our study focuses on implementing an algorithm for solving railway planning with predictable or unexpected demands. The system modeling process uses P-time stochastic Petri nets. In order to improve the efficiency and punctuality of rail transport. A method for minimizing disruptions by managing maintenance is proposed. The paper's contribution is a scheduling algorithm that prioritizes maintenance tasks according to a dynamic criticality. This dynamic criterion incorporates real-time information provided by a supervisory rail system layer (a SCADA system). The STPN is used for modeling system operation. It supplies a mathematical framework for disturbance detection and prognosis. The presented work constitutes a first step towards adapting the digital twin concepts for the maintenance of weakly instrumented railway infrastructures.

关键词： Rails Job shop scheduling Heuristic algorithms SCADA systems Dynamic scheduling Prediction algorithms Rail transportation

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Optimization of Coupled ARX-Laguerre Multimodel Parameters Using Genetic Algorithm

Optimization of Coupled ARX-Laguerre Multimodel Parameters U...

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International Conference on Systems and Control (ICSC)

作者： Hajer Benamor Marwa Yousfi Larbi Chrifi Alaoui Tarek Garna Laboratory of Automatic Signal and Image Processing (LARATSI) National Engineering School of Monastir Monastir Tunisia Laboratory of Innovative Technology (LTI UR-UPJV 3899) University of Picardie Jules Verne Amiens France Higher Institute of Applied Science and Technology of Sousse Sousse Tunisia

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

This work treats parametric optimization of nonlinear systems using genetic algorithms. The authors focus, in this paper, in combining the ARX-Laguerre model and the multimodel approach with its coupled configuration to develop a model named the coupled ARX-Laguerre multimodel. The proposed model is characterized by its Laguerre poles, Fourier coefficients and weighting functions parameters. The complexity of the model is tightly related to the optimal identification of the Laguerre poles characterizing the model. The authors propose an optimization algorithm of the multimodel parameters (Laguerre poles and weighting functions parameters) based on the genetic to guarantee a significant parametric number reduction with regards to the classic ARX multimodel.

关键词： Uncertainty Solids Genetics Control systems Chemical reactions Iterative algorithms Complexity theory Nonlinear systems Optimization Genetic algorithms

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Fault detection based on Principal Component Analysis in the context of Missing Data

Fault detection based on Principal Component Analysis in the...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Yosra Fatnassi Ines Jaffel Mohamed Guerfel Nasreddine Bouguila Laboratory of Automatic Signal and Image Processing (LARATSI) National School of Engineers of Monastir University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Kairouan Universiy of Kairouan Tunisia Higher Institute of Applied Sciences and Technology of Sousse Universiy of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Improving fault detection and diagnosis (FDD) in systems with incomplete features is a prevalent challenge in industrial applications. In this context, two distinct approaches (k-means clustering and k-nearest neighbor imputation KNN) for addressing Missing Data (MD) were presented and compared. The evaluation was conducted with introducing of MD, ranging from a 10% to 50%. The proposals aim to enhance the monitoring and diagnosis of the system through the utilization of principal component analysis (PCA) for feature extraction an fault detection.

关键词： Error analysis Fault detection Feature extraction Chemical reactions Imputation Reliability Proposals

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Robust multiple model-based control proposed for a SISO CSTR Benchmark 2

Robust multiple model-based control proposed for a SISO CSTR...

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2nd IEEE International Conference on signal, Control and Communication, SCC 2021

作者： Yousfi, Marwa Garna, Tarek Messaoud, Hassani University of Monastir Research Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Monastir5019 Tunisia University of Sousse Higher Institute of Applied Science and Technology of Sousse Sousse4003 Tunisia

ISBN: (纸本)9781665402705

In this paper, robust multiple model-based LSDP (Loop-Shaping Design Procedure) controller is proposed for a SISO system where each linear submodel is represented in the form of a SISO ARX (Auto Regressive with exogenous input) Laguerre model. The proposed controller guarantees robustness in term of stability and reference tracking of the SISO system in presence of parametric uncertainties. Simulation example on the SISO CSTR (Continuous Stirred Tank Reactor) Benchmark is given to show the efficiency and the robustness of the proposed controller in handling the parametric uncertainties of the process. © 2021 IEEE.

关键词： Controllers

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Enhanced Genetic Algorithms Based Optimization of ARX-Laguerre Model Parameters: Application to 2-Dof Helicopter

Enhanced Genetic Algorithms Based Optimization of ARX-Laguer...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Mohamed Guerfel Imen Ben Abdelwahed Ines Jaffel Kais Bouzrara Electronics department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Kairouan University of Kairouan Tunisia

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the impact of noise on it is addressed. A new methodology for optimizing Laguerre poles using genetic algorithms is proposed. It is used in modeling a real 2-Degree-of-Freedom helicopter offering valuable insights for real-world applications.

关键词： Green energy Helicopters Noise 2-DOF Transfer functions Noise measurement Optimization Genetic algorithms

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Exoskeleton Dynamic Modeling and Identification with Load and Temperature-Dependent Friction Model

Exoskeleton Dynamic Modeling and Identification with Load an...

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signal, Control and Communication (SCC), International Conference on

作者： Mouna Dali Hassen Imen Laamiri Hassani Messaoud Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia

ISBN: (纸本)9781665402712

Frictional losses within the transmission components of robotic manipulators are often modeled as a constant Coulomb friction force or/and a viscous friction force proportional to the sliding velocity. However, the variation in contact forces of lubricated surfaces of the mechanical transmission system influences the friction force. The joint temperature also has an effect on the variation of the latter. Hence, the importance of considering the parameters mentioned in the dynamic model of the robotic manipulator. This paper presents a new load and temperature-dependent model that will be subsequently identified from experimental measurements carried out on two-link robotic manipulator with different loads.

关键词： Temperature measurement Temperature dependence Friction Force Exoskeletons Dynamics Propagation losses

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Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis

Implementation of Genetic Algorithm Optimization based Artif...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Amal Hichri Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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