Unit of Research Control

Tunis, Tunisia

Unit of Research Control

Tunis, Tunisia
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Guesmi H.,University of Monastir | Guesmi H.,Jazan University | Salem S.B.,Unit of Research Control | Bacha K.,Unit of Research Control
Computers and Electrical Engineering | Year: 2015

Online induction machine faults diagnosis is a concern to guarantee the overall production process efficiency. Nowadays, the industry demands the integration of smart wireless sensors networks (WSN) to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can produce sever damages. The origin of most recurrent faults in rotary machines is in the components: stator, rotor, bearing and others. This work presents a novel methodology for the online faults diagnosis in induction motors. This technique uses the smart WSN to obtain the machine condition based on the motor stator current analysis. The implementation of the proposed smart sensor methodology allows the system to perform online fault detection in a fully automated way. Simulation results presented show the efficiency of the proposed method to detect simple and multiple faults in induction machine. It provides detailed analysis to address challenges in designing and deploying WSNs in industrial environments, and its reliability. © 2014 Elsevier Ltd. All rights reserved.


Ben Salem S.,Unit of Research Control | Bacha K.,Unit of Research Control | Gossa M.,Unit of Research Control
Proceedings of the Mediterranean Electrotechnical Conference - MELECON | Year: 2012

In this work we propose an original failure signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can release two failure signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of HMCSV and HPCSV spectrums are described and the related frequencies determined. This analysis offers an easy interpretation to conclude on the induction motor condition and its voltage supply state. The proposed signature shows its effectiveness and its robustness in both electrical and mechanical fault detection. This approach was applied to a 1.1 kw induction motor under normal operation and with the following fault: voltage unbalanced, broken rotor bar, air-gap eccentricity and ball bearing defect. © 2012 IEEE.


Ben Salem S.,Unit of Research Control | Bacha K.,Unit of Research Control | Gossa M.,Unit of Research Control
Proceedings of the Mediterranean Electrotechnical Conference - MELECON | Year: 2012

In this work we propose an original failure signature based on the current Hilbert-Park vector pattern analysis. The advantage of this approach is which does not require a long temporal recording, and their processing is simple. This analysis offers an easy interpretation to conclude on the induction motor condition and its voltage supply state. The proposed signature shows its robustness and its power especially in the case of unloaded machine. This approach was applied to a 1.1 kw induction motor under normal operation and with the following fault: voltage unbalanced, broken rotor bar, air-gap eccentricity and ball bearing defect. © 2012 IEEE.


Ben Salem S.,Unit of Research Control | Bacha K.,Unit of Research Control | Chaari A.,Unit of Research Control
ISA Transactions | Year: 2012

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. © 2012 ISA.


Ben Salem S.,Unit of Research Control | Touti W.,Unit of Research Control | Bacha K.,Unit of Research Control | Chaari A.,Unit of Research Control
2013 International Conference on Electrical Engineering and Software Applications, ICEESA 2013 | Year: 2013

In this work we have shown that the extended Park's vector spectrum is rich in harmonics characteristics of mechanical defects (air-gap eccentricity and outer raceway bearing fault). About the use of Park's Lissajou's curves to identify mechanical defects, we have demonstrated that this type of index can only detect the occurrence of a fault, but it cannot identify. © 2013 IEEE.


Souahlia S.,Unit of Research Control | Bacha K.,Unit of Research Control | Chaari A.,Unit of Research Control
International Journal of Electrical Power and Energy Systems | Year: 2012

Dissolved gas analysis (DGA) is a widely-used method to detect the power transformer faults, because of its high sensitivity to small amount of electrical faults. The DGA is exploited for fault classification tools implementation using the artificial intelligence techniques. In this study, we use the Rogers ratios, the Doernenburg ratios methods and our proposed combination of Rogers and Doernenburg ratios DGA methods as gas signature. The multi-layer perceptron neural network (MLPNN) is applied for decision making. The paper presents a comparative study on one hand for the choice the most appropriate DGA method and to resolve the problem of conflict between the Rogers and Doernenburg ratios methods. On the other hand, it compares the various MLP architectures by comparing two output data types and three hidden layer types with the aim to establish the most appropriate MLP model. Before testing, the proposed structures are trained and tested by the experimental data from Tunisian Company of Electricity and Gas (STEG). The test results suggest that MLPNN ratios combination can generalize better than other MLPNN models. The approach has the advantages of high accuracy. The other advantage is that the model is practically applicable and may be utilized for an automated power transformer diagnosis. The classification accuracies of the MLPNN classifier are compared with fuzzy logic (FL), radial basis function (RBF), K-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification. © 2012 Elsevier Ltd. All rights reserved.


Bacha K.,Unit of Research Control | Salem S.B.,Unit of Research Control | Chaari A.,Unit of Research Control
International Journal of Electrical Power and Energy Systems | Year: 2012

In this work we propose an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can release two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two signatures are subsequently analyzed using the classical fast Fourier transform (FFT). The effects of HMCSV and HPCSV spectrums are described and the related frequencies are determined. A comparative study is presented of the suggested signature (HPCSV) and the MCSA which is the signature more recently proposed in the literature. The proposed signature shows its effectiveness and its robustness in both electrical and mechanical fault detection. The magnitudes of spectral components relative to the studied faults are extracted in order to develop the input vector necessary for the pattern recognition tool based on support vector machine (SVM) approach with an aim of classifying automatically the various states of the induction motor. This approach was applied to a 1.1 kw induction motor under normal operation and with the following faults: unbalanced voltage, broken rotor bar, air-gap eccentricity and outer raceway ball bearing defect. © 2012 Elsevier Ltd. All rights reserved.


Bacha K.,Unit of Research Control | Souahlia S.,Unit of Research Control | Gossa M.,Unit of Research Control
Electric Power Systems Research | Year: 2012

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA). Support vector machine (SVM) is powerful for the problem with small sampling (small amounts of training data), nonlinear and high dimension (large amounts of input data). The standard IEC 60599 proposes two DGA methods which are the ratios and graphical representation. According the experimental data, for the same input data, these two methods give two different faults diagnosis results, what brings us to a problem. This paper investigates a novel extension method which consists in elaborating an input vector establishes by the combination of ratios and graphical representation to resolve this problem. SVM is applied to establish the power transformers faults classification and to choose the most appropriate gas signature between the DGA traditional methods and a novel extension method. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to illustrate the performance of proposed SVM models. Then, the multi-layer SVM classifier is trained with the training samples. Finally, the normal state and the six fault types of transformers are identified by the trained classifier. In comparison to the results obtained from the SVM, the proposed DGA method has been shown to possess superior performance in identifying the transformer fault type. The SVM approach is compared with other AI techniques (fuzzy logic, MLP and RBF neural network); the proposed method gives a good performance for transformers fault diagnosis. The test results indicate that the novel extension method and the SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification. © 2011 Elsevier B.V. All rights reserved.


In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.


PubMed | Unit of Research Control
Type: Journal Article | Journal: ISA transactions | Year: 2012

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

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