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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

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Nonlinear model based predictive control using multiple models approach expanded on Laguerre bases

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WSEAS Transactions on Systems and Control 2015年 10卷 113-126页

作者： Marouani, Ghassen Mbarek, Abdelkader Garna, Tarek Messaoud, Hassani Research Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Rue Ibn ELJazzar 5019 Monastir Tunisia

This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use the provided Laguerre multiple model (LMM) to synthesize a NMPC algorithm. The proposed pole optimization technique as well as the NMPC using LMM approach are validated on a chemical reactor. © 2015 World Scientific and Engineering Academy and Society. All rights reserved.

关键词： Nonlinear systems

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Identification of optimal kernel parameters of RKHS model based on genetics algorithm

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WSEAS Transactions on Systems and Control 2015年 10卷 373-384页

作者： Taouali, Okba Tawfik, Najeh Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Rue Ibn El Jazzar Monastir Tunisia

This paper proposes a new approach based on the Genetics Algorithm to determine the optimal kernel parameters of the Reproducing Kernel Hilbert Space (RKHS) model. These parameters are the width of the kernel function and the regularization parameter. The proposed meta-method has been tested to model some benchmarks such a benchmark of DC-Motor [21], a Wiener-Hammerstein benchmark [17], a Feedback’s Process Trainer PT326 [20] and a Continuous Stirred Tank Reactor CSTR [22] and the results are satisfactory. © 2015, World Scientific and Engineering Academy and Society. All rights reserved.

关键词： DC motors

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Robust parametric estimation based on MIMO ARX Laguerre model

Robust parametric estimation based on MIMO ARX Laguerre mode...

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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 Tunisia

We propose, in this paper a new identification technique of MIMO linear systems. This technique consists on the extension of the robust identification technique entitled Unknown But Bounded Error (UBBE) to multivariable linear systems, using reduced complexity MIMO ARX decomposed on independent orthonormal Laguerre bases. The decomposed model is entitled MIMO ARX Laguerre model, we formulate the UBBE principle approach in the multivariable linear systems case to facilitate updating the parameter uncertainty set. This set can be approximated to an ellipsoid with outer bounding (EOB). The proposed approach is tested on numerical example and simulation results show its effectiveness.

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Reduced Gaussian process regression for fault detection of chemical processes

Reduced Gaussian process regression for fault detection of c...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Fezai, Radhia Mansouri, Majdi Bouguila, Nasreddine Nounou, Hazem Nounou, Mohamed Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar Chemical Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

In this paper, a reduced Gaussian process regression (RGPR)-based generalized likelihood ratio test (GLRT) is proposed for fault detection in industrial systems. In contrast to the classical GPR technique, the RGPR model can handle domains with a large number of activities that require very large training sets. The developed RGPR-based GLRT method aims first to build a RGPR model, then, it consists to apply GLRT to the monitored residuals obtained from PGPR for fault detection purposes. The fault detection performance of the developed RGPR-based GLRT method is illustrated through the Tennessee Eastman process. The simulation results show that the RGPR-based GLRT method outperforms the conventional GPR-based GLRT technique in terms of miss detection rate and CPU-time. © 2019 IEEE.

关键词： Fault detection

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Robust identification of nonlinear systems represented by second order NARX-Laguerre model

Robust identification of nonlinear systems represented by se...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Imen Ben Abdelwahed Ameni El Anes Kais Bouzrara Research Laboratory of Automatic signal and Image processing (LARATSI) National Engineering School of Monastir (ENIM) University of Monastir Tunisia

This paper discusses the robust identification issue of the reduced second order NARX-Laguerre model. The latter model, result of the expansion of the second order NARX model parameters on independent Laguerre bases, is characterized by five parameters called Laguerre poles. The optimal values of these poles are identified using Genetic Algorithm. Special attention is given to the UBBE (Unknown But Bounded Error) approach. This technique involves updating the domain to which belong the Fourrier Coefficients in order to achieve a membership set which is a set of parameter vectors consistent with the structure of the model and the bounds of the errors. The proposed technique is illustrated by a simulation example.

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Machine learning based Gaussian process regression for fault detection of Biological Systems

Machine learning based Gaussian process regression for fault...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

ISBN: (纸本)9781728151847

Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine. © 2019 IEEE.

关键词： Fault detection

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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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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

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

ISBN: (纸本)9798350389838

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. © 2024 IEEE.

关键词： Genetic algorithms

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

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

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

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

ISBN: (纸本)9781665402712

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.

关键词： Uncertainty Perturbation methods Process control Benchmark testing Numerical simulation Robustness Stability analysis

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