Automatique
Support Vector Machine Based on Firefly Algorithm For Bearing Fault Diagnosis
The fault diagnostics and identification of rolling element bearings have been the subject of extensive research. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by signal’s time-varying statistical parameters. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, Firefly Algorithm (FFA) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach. Voir les détails
Mots clés : Condition monitoring, Firefly Algorithm, Roller Bearing, Statistical parameters, Support Vector Machine.
Autoregressive Modeling and PCA Preprocessing to Support Vector Machines Based on PSO for Bearing Fault Diagnosis
In this paper a method for fault diagnosis of rolling bearings is presented. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper Autoregressive Modeling followed by Principal Components Analysis (PCA) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by AR-PCA, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach. Voir les détails
Mots clés : Autoregressive Modeling, Principal Components Analysis, Support vector machine, Particle Swarm Optimization, Wavelet Packet, Fault Diagnosis, Roller Bearing.
Feature Extraction and SOM for Bearing Fault Diagnosis
In this paper a method for fault diagnosis of rolling bearings is presented. It consists of two parts: vibration signal feature extraction and condition classification. The aim of the first step is the extraction of the relevant parameters; the proposed technique consists of preprocessing the bearing fault vibration signal using a combination of the signal’s Kurtosis and discrete wavelet transform (DWT). The Self-organization Map (SOM) is used to accomplish the classification step and automate the fault diagnosis procedure. The results have shown feasibility and effectiveness of the proposed approach. Voir les détails
Mots clés : Condition monitoring, Discrete wavelet transform, Fault Diagnosis, Kurtosis, Roller Bearing, Rotating machines, Self-organization Map, Vibration measurement.
Direct adaptive backstepping control with tuning functions for a single-link flexible-joint robot
In this paper, direct adaptive backstepping control with tuning functions approach for a single-link flexible-joint robot model is proposed. The proposed approach of adaptation is based on the tracking error based parameter adaptation law. First, the direct adaptive backstepping control with tuning functions is applied for a class of nonlinear systems in parametric strict-feedback form to avoid overparametrization. Next, the main steps of the controller design for a single-link flexible-joint robot manipulator model are described. The stability of the proposed controller is studied by using the Lyapunov functions. Finally, the simulation results are given to demonstrate the performance of the proposed approach. Voir les détails
Mots clés : Single-link flexible-joint robot, Backstepping control, direct adaptive control, tuning functions, direct adaptation
Indirect adaptive backstepping control by using the virtual controls filtering
In this paper, by using the dynamic surface control technique, an indirect adaptive backstepping controller based x-swapping identifier with a gradient-type update law is proposed for a class of parametric strict-feedback nonlinear systems. The main steps of the controller design for a class of nonlinear systems in parametric strict-feedback form are described. Then, the closed-loop error dynamics is shown to be globally stable by using the Lyapunov stability approach. Simulation results for a single-link flexible-joint robot manipulator are given to illustrate the performance of the proposed controller. Voir les détails
Mots clés : Backstepping control, adaptive control, dynamic surface control, Lyapunov stability, Single-link flexible-joint robot
Adaptive backstepping control using combined direct and indirect σ–modification adaptation
In this paper, by using the dynamic surface control technique, an adaptive backstepping controller using combined direct and indirect σ-modification adaptation is proposed for a class of parametric strict-feedback systems. In this approach, a σ-modification parameter adaptation law that combines direct and indirect update laws is proposed. At first, the x-swapping identifier with a gradient-type update law is presented for a class of parametric strict-feedback nonlinear systems. Next, the main steps of the controller design for a class of nonlinear systems in parametric strict-feedback form are described. The closed-loop error dynamics is shown to be globally stable by using the Lyapunov stability approach. Finally, simulation results for a single-link flexible-joint robot manipulator are given to illustrate the tracking performance of the proposed adaptive control scheme. Voir les détails
Mots clés : Backstepping control, direct and indirect adaptive control, adaptive dynamic surface control, Lyapunov stability, flexible joint manipulators
Condition monitoring of rotating machinery by vibration signal processing methods
La détection et le diagnostic de défauts jouent un rôle important dans la sécurité, la productivité et la qualité des pro-duits. Durant la dernière décennie, des recherches actives et considérables se sont effectuées en vue de développer des mé-thodes de détection et de diagnostic de défauts des machines tournantes. Dans ce travail, des techniques d’analyse dans les domaines : temporel, fréquentiel et temps-fréquence, sont étu-diées. Des résultats en utilisant des mesures réelles, sont pré-sentés et discutés. Voir les détails
Mots clés : diagnostic de défauts, machines tournantes, domaine temporel, domaine fréquentiel, domaine temps-fréquence.
Vibration signal-based bearing fault diagnosis usingoptimized multi-scale entropy and ANFIS network
This paper presents an application of a multi-scaleclassification method to detect bearing-related faults in anexperimental benchmark. Multi-scale analysis of the vibrationsignal allows the representation of nonlinear dynamics andcoupling effects between different mechanical components ofindustrial equipment. An improved multi-scale entropy analysisis used as features extraction tool for the diagnosis procedure.The classification of the state of health of the bearings is achievedusing adaptive neuro-fuzzy inference system and neural networksfor different faults scenarios with variable fault severity.Experimental results show the importance of the choice of thefeatures extraction method for the classification of faults and thedetermination of their severity. Voir les détails
Mots clés : Fault Diagnosis, Vibration analysis, multi-scale entropy (MSE), bearing faults
Profile of heat transfer between two different materials
In this article we made a study on the transfer of heatin two different materials such as steel and copper. Initially wepresent the profile of heat transfer in each material alone, after wepresent the profile of the same phenomena in the two materials incontacts, and to see how the temperature is propagated when we havea variation of thermal conductivity. The simulation is made byANSYS software which permits us to solve the energy equation byfinite element method. Voir les détails
Mots clés : Simulation; material; heat transfer.
Sliding Mode Control Based Bacterial Foraging Optimization of Wind Energy Conversion Systems
In recent years, the energy production by wind turbines has been increasing, because its production is environmentally friendly. In this paper, Bacterial Foraging Optimization (BFO) is proposed to generate a Sliding Mode Controller. The Sliding Mode Control (SMC) is proposed to control a squirrel-cage induction generator (SCIG) in order to maximize power captured by wind energy conversion system applied to the welding system. Simulation studies are made with Matlab / Simulink to verify the effectiveness of the purposed method. Voir les détails
Mots clés : Squirrel Cage Induction Generator (SCIG), Wind Energy Conversion System (WECS), Sliding Mode Control (SMC), Bacterial Foraging Optimization (BFO), Welding System