Lotfi Nabli

Modelling and monitoring highway traffic

This paper deals with a detection approach that tries to solve major problems of traffic flow by combining a higher order modeling tool and the principal components analysis. This modeling system is applied to the data preprocessing level. The PCA is introduced for fault detection by comparing the method of SPE and Hotteling T2. For fault isolation, calculating contributions enables identify faulty variables.

Nonlinear system monitoring using multiscaled principal components analysis based on neural network

In this paper, we propose a new method based on multiscaled principal component analysis for nonlinear systems analysis. We introduce nonlinear PCA based on neural networks and discrete wavelet transform. The data matrix describing a nonlinear process is decomposed into five wavelet resolution levels. The neural PCA is applied to each coefficient of details and approximations; we select only the scales having a defect to reconstruct the data matrix. Neural PCA is again applied to the new matrix to determine the defective variables, which are detected using the square predictive error (SPE) statistic and identified using the contributions calculation method. This method is applied to a biological process and shows efficient results.

Hybrid Monitoring for the Prognostic of the Reliability System

This paper aims at developing a model for the monitoring of a rotating machine. The contribution in this model is the development of the last stage of monitoring which is the prognostic; indeed, the method used takes into account the required quality criteria of the product on one hand and the state of the current system of one somewhere else on the other. The objective of this contribution is to estimate the reliability of the system in time and to plan the time of total system dysfunction. The interval constrained Petri nets are used for the modeling of an industrial example which is the centrifuge pump. Copyright

Development of a structuring residuals method for diagnostic by PCA-based genetic algorithms

In this paper we propose a new method of structuring of residuals for detection and fault localization by the PCA (Principal Component Analysis). This method is based on the multi-objective optimization by genetic algorithms. The principle of this approach is to maximize the sensitivity of some residuals at certain defects and simultaneously minimize the sensitivity of the remaining residuals for other defects. Numerical example is simulated to validate the proposed technique.

Statistical method for ECG analysis and diagnostic

In this paper, a statistical method of ECG analysis and diagnostic is proposed. This method is based on three parts: data simplification using multiscaled PCA, faults detection and localisation by introducing classic linear PCA. The studied data is presented as a multivariate matrix. The variables of this matrix are extracted from the ECG waves characteristics: waves amplitudes and segments measurements. The developed approach allows detecting arrhythmias and heart beat troubles. Comparing the results obtained by this approach and the data of the expert, we approve the performance of our study.

Reconfigurable manufacturing system: Overview and proposition of new approach

To confront the rapid change in manufacturing environments, companies need a Reconfigurable Manufacturing System (RMS) which combines the high production volume of Dedicated Manufacturing Lines (DML) with the large variety of products of Flexible Manufacturing System (FMS). In this paper, we propose a state of art of recent research work on RMS. After highlighting the advantages of RMS compared to existing manufacturing systems, we outline different issues discussed in the context of RMSs and what it brings as solutions. Finally we present the main idea of a new approach for reconfiguration process following a predictive monitoring.

Industrial process defect classification by exploiting PCA and fuzzy logic

This This paper describes a proposed monitoring approach destined for industrial process using the principal components analysis (PCA) and fuzzy logic. The aim of our work is to detect and locate defaults using PCA and then classifying the existed problems in terms of gravity using fuzzy logic. We introduce a historic data in the form of a matrix of m variables and N observations: measures were taken of the various existing sensors in the process. In our case, we study the quality criteria for flour production process. The obtained results are effective by comparing them with expert data.

New methods of Laguerre pole optimization for the ARX model expansion on Laguerre bases

The ARX-Laguerre model is a very important reduced complexity representation of linear system. However a significant reduction of this model is subject to an optimal choice of both Laguerre poles. Therefore we propose in this paper two new methods to estimate, from input/output measurements, the optimal values of Laguerre poles of the ARX-Laguerre model. The first method is based on the Newton-Raphsons iterative technique where we prove that the gradient and the Hessian can be expressed analytically. The second method is based on Genetic Algorithms. Both proposed algorithms are tested on a numerical example and on a heating benchmark.

A new approach based on Global Velocity Particle Swarm Optimization to solve job-shop scheduling problems, PSO-VG-JSSP

This paper deals with the Job-shop scheduling problem. We propose to solve this problem by exploiting the Particle Swarm Optimization Global Velocity (PSOVG) algorithm. The PSOVG by its nature focus on the global optimum within a given set of solutions. In this paper a solution is PSO particle, it consists in a possible scheduling solution for the given problem. The PSO-VG-JSSP is a PSO-VGO with a constraints control policy embedded in, allowing to detect and remove non admissible solutions. In this paper the particle representing a non admissible solution is simply removed and replaced by a new random particle.

PSO for Job-Shop Scheduling with Multiple Operating Sequences Problem - JS

This paper focus on a complex problem of job shop scheduling where each jobs have a multiple possible operations sequences. The resolution of this type of problem has not been treated in the literature. To solve this, a new algorithm based on Particle Swarm Optimization Global Velocity (PSOVG) was proposed. The objectif is to minimize the makespan. The simulation results show the efficiency of our proposed approach.