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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.


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 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 | 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 | 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.

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