Kaouther Laabidi

Synthesis of a fractional PI controller for a first-order time delay system

This paper considers the problem of stabilizing a first-order plants with known time delay using a fractional-order proportional–integral controller P I λ . Using a generalization of the Hermite–Biehler theorem applicable to quasi-polynomials, a complete analytical characterization of all stabilizing gain values ( k p , k i ) is provided. The widespread industrial use of fractional PI controllers justifies a timely interest in P I λ tuning techniques.

Feature Selection using an SVM learning machine

In this paper we suggest an approach to select features for the Support Vector Machines (SVM). Feature selection is efficient in searching the most descriptive features which would contribute in increasing the effectiveness of the classifier algorithm. The process described here consists in backward elimination strategy based on the criterion of the rate of misclassification. We used the tabu algorithm to guide the search of the optimal set of features; each set of features is assessed according to its goodness of fit. This procedure is exploited in the regulation of urban transport network systems. It was first applied in a binary case and then it was extended to the multiclass case thanks to the MSVM technique: Binary Tree.

Tennessee Eastman Process Diagnosis Based on Dynamic Classification With SVDD

The support vector domain description (SVDD) is an efficient kernel method inspired from the SV machine (SVM) by Vapnik. It is commonly used for one-classification problems or novelty detection. The training algorithm solves a constrained convex quadratic programming (QP) problem. This assumes prior dense sampling (offline training) and it requires large memory and enormous amounts of training time. In this paper, we propose a fast SVDD dedicated for multiclassification problems. The proposed classifier deals with stationary as well as nonstationary (NS) data. The principle is based on the dynamic removal/insertion of informations according to adequate rules. To ensure the rapidity of convergence, the algorithm considers in each run a limited frame of samples for the training process. These samples are selected according to some approximations based on Karush–Kuhn–Tucker (KKT) conditions. An additional merge mechanism is proposed to avoid local optima drawbacks and improve performances. The developed method is assessed on some synthetic data to prove its effectiveness. Afterward, it is employed to solve a diagnosis problem and faults detection. We considered for this purpose a real industrial plant consisting in Tennessee Eastman process (TEP).

Meanshift Clustering Based Trend Analysis Distance for Fault Diagnosis

This paper describes a new technique for clustering data based on their trend characteristics. The technique that we propose proceed by incorporating a new distance based on qualitative trend analysis into Mean shift clustering algorithm. Mean shift clustering is a powerful non-parametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. Trend analysis is a data-driven semi-quantitative technique that has been used for process monitoring and fault detection and diagnosis. The performances of our approach are assesed through synthetic banana shaped data. Unsupervised clustering is then applied for intelligent decision-making process specifically for fault diagnosis on Tennessee Easteman Process (TEP) challenge.

A nonlinear MIMO system identification based on improved Multi-Kernel Least Squares Support Vector Machines (Improved Multi-Kernel LS-SVM)

In this paper, a new method for the identification of nonlinear Multiple Input-Multiple Output (MIMO) systems is proposed. An improved Multi-Kernel Least Squares Support Vector Machines (Improved Multi-Kernel LS-SVM) based on Constrained Particle Swarm Optimization (CPSO) is given. The basic LS-SVM idea is to map linear inseparable input data into a high dimensional linear separable feature space via a nonlinear mapping technique (kernel function) and to carry out linear classification or regression in feature space. The choice of kernel function and the corresponding parameters is an important task which is related to the system nonlinearity degrees. The suggested approach combines several kernels in order to take advantage of their performances. The CPSO technique is used to give solution for the determination of optimized kernel parameters and their evolved weights. Simulation results show that the CPSO can quickly obtain the optimal parameters and therefore satisfying the required precision.

New approach for systems monitoring based on semi-supervised classification

In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both supervised and unsupervised learning methods. Since supervised learning requires necessarily a broad labelled base that may not always available in a sufficient cardinality, we aim at first an unsupervised grouping of a critical faults set (classes) though a Self-Adaptive Clustering Algorithm (SACA). Within this framework, the presented algorithm is based on the evaluation of a metric distance between cluster centroids and samples. An integrated process for optimization allows the tuning of confidence threshold for decision. Next, an additional supervised classification step using Artificial Neural Network (ANN) provides practical information for decision-making. The network is trained according to the classification multi-levels dedicated for multi-class problems. The developed approach is assessed on a hydraulic system consisting of three connected tanks.

Faults classification of asynchronous machine based on the probabilistic neural network (PNN)

In this paper, a probabilistic neural network (PNN) based approach for fault diagnostic in case of faults affecting asynchronous machines is proposed. Performances of the proposed probabilistic neural network are compared to those of Multi-Layer Perceptron (MLP) and Generalized Regression Neural Network (GRNN), using a cross-validation procedure to provide better generalization of neural network classifiers. To perform efficient diagnostic results the cross-validation procedure input data is partitioned into three sets: a training set, a validation set and a test set. The stator RMS values of three-phase voltages and currents are used as model inputs to identify the different types of faults and the normal operating mode.

Systems monitoring based on dynamic classification with SVDD

In this paper, we address the problem of system monitoring and faults detection using classification-based approach. The main is to follow online evolutions which can occur on the diagnosed system in the course of time. In data classification, the functioning modes are represented with a set of similar patterns called classes. These classes change their intrinsic characteristics and they are likely to be dynamic. Indeed, over the time, new data may be incorporated into the informative model and some others can be likewise discarded. In this paradigm, we propose a dynamic classification method based on modified version of the Support Vector Domain Description (SVDD). Thanks to added insertion/removal procedure, the employed SVDD supports evolving data and maintains dynamically the descriptive model. The proposed approach is applied afterwards on a hydraulic system consisting of three interconnected tanks.

Feature extraction for atmospheric pollution detection

Atmospheric data sets are represented by an amount of heterogeneous and redundant data. As number of measurements grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. The aim of this work is to propose a feature extraction technique based on construction of clusters of similar features. The main objective of the proposed process is to attempt to reach a more accurate classification task and to achieve a more compact representation of the underlying structure of the data. The paper reports the results obtained using the above extraction and analysis procedure of a real data set on atmospheric pollution. It is shown that the proposed approach is able to detect underlying relationship between features and thus get to ameliorate classification accuracy rate.

Synthesis of PI fractional controller for fractional systems with time delay

A fractional mathematic analysis for characteristic equations defining fractional order system with time delay is proposed in this paper. Such analysis is based on an extension of Hermite-Biehler theorem applicable to quasipolynomials. A crucial step in applying of Hermite-Biehler theorem is to ensure that the real and imaginary parts of the characteristic equation have only real roots, such a property can be ensured by using Pontryagin theorem. The range of the values of the parameters Kp and Ki that fulfil both the conditions of the Hermite-Biehler theorem represent the complete set of the stabilizing PIλ=°·5 parameters. Step responses of the closed-loop system are calculated in the boundaries of the the established stability region to confirm the obtained results.