A new frequency analysis for diagnosis of bearing defects in induction motors using the adaptive lifting scheme of wavelet transforms

This work describes a novel and effective application of the adaptive wavelet transform for the detection of bearing faults on induction motor stator current. This transform is based on a three-step nonlinear lifting scheme: a fixed prediction followed by a space-varying update and a no additive prediction. This transformation technique is used in a diversity of applications in digital signal processing and the transmission or storage of sampled data (notably the compression of the sound, or physical measurements of accuracy). Many faults in induction motor have been identified as bearing defects, rotor defects and external defects. Experimental results confirm the utility and the effectiveness of the proposed method for outer raceway fault diagnosis under no load and full load conditions

Towards 5G wireless systems: A modified Rake receiver for UWB indoor multipath channels

This paper presents a modified receiver based on the conventional Rake receiver for Ultra-Wide Band (UWB) indoor channels of femtocell systems and aims to propose a new solution to mitigate the multipath phenomenon. Furthermore, this work proposes an upgrade for the conventional Rake receiver to fulfill the needs of 5G wireless systems through a new concept named “hybrid femtocell” that joins UWB with millimeter wave (mmWave) signals. The modified receiver is considered to be a part of the UWB/mmWave hybrid femtocell system, where it is developed for confronting the indoor multipath channels and to ensure a flexible transmission based on an Intelligent Controlling System (ICS). Hence, we seek to exploit the circumstances when the channel is less complex to switch the transmission to a higher data rate through higher M-ary Pulse Position Modulation (PPM). Furthermore, an ICS algorithm is proposed and an analytical model is developed followed by performance studies through simulation results. The results show that using the UWB technology through the modified receiver in femtocells could aid in mitigating the multipath effects and ensuring high throughputs. Thus, the UWB based system promotes Internet of Things (IoT) devices in indoor multipath channels of future 5G

DEUX APPROCHES DE NORMALISATION DES ENTREES POUR LA RECONNAISSANCE DE MOTS ISOLES

Dans cet article, nous allons présenter deux systèmes de reconnaissance de chiffres parlés anglais, en mode indépendant du locuteur, basé sur les deux stratégies principales de la classifi- cation binaire SVM multi-classes. Cependant, les techniques SVM exigent des vecteurs d'entrée de taille fixe. Pour lever cette difficulté, nous avons utilisé deux approches différentes norma- lisation des entées basées sur le fenêtrage fixe et variable des vecteurs acoustiques des énoncés d’entrées. Le but est de réduire le temps de calcul pendant la phase d’apprentissage et de test des deux stratégies et déterminer ainsi celle qui donne le meilleur taux de reconnaissance. Les résultats trouvées montrent que la chaîne de reconnaissance utilisant la stratégie un contre un comme moteur de reconnaissance et l’approche fenêtre de taille fixe pour normaliser les entrées est beaucoup plus satisfaisante par rapport aux autres chaînes présentées dans notre article. Ce système de reconnaissance atteint un taux de 98,95%, tout en utilisant seulement 13 vecteurs caractéristiques par énoncé en entrées du classifieur, ce qui réduit considérablement le temps d’apprentissage et de tes

Comparative Study between Two Diagnostic Techniques Dedicated to the Mechanical Fault Detection in Induction Motors

Among the main causes which have a great influence on the service life of rotating electrical machines are mechanical faults. The squirrel cage induction motors are one of the most important induction motors in industrial and modern industrial applications. The reasons are the low cost, robustness, and low maintenance. In general fault causes unwanted vibrations in rotating machines and the rolling element bearing faults affect the induction machines with a significant percentage. It is a major problem among different faults because it can cause catastrophic damage. Early and accurate detection of rolling element bearing faults are essential for both effective fault management and fault isolation. This paper presents a detailed study of rolling element bearing faults in squirrel cage induction motors using two recent diagnostic techniques. The first technique uses the stator current signal based on the fast Fourier transform. According to this technique, it is carefully checked the spectral content of the stator current. The second technique uses the stray flux signature analysis. Experimental tests for different conditions as low load, full load operation, healthy and faulty states of the induction motors have been performed. So, a detailed comparison between the two techniques led us to achieve a judicious decision about the rolling element bearing faults detection

Rolling Bearing Failure Detection in Induction Motors using Stator Current, Vibration and Stray Flux Analysis Techniques

International audienceMany industrial applications use the induction machine for its advantages, like robustness. But like any other machines, it can be affected by several faults such as broken rotor bars, stator inter-turn short-circuit, bearing faults, etc. On the one hand, mechanical faults produce vibration, eccentricity and torque oscillations which influence the stator current and the distribution of the magnetic field. Therefore, early detection of mechanical faults leads to avoid damage or sudden stop of the induction machine. In this context, this paper studies the performances of three fault detection and diagnostic techniques for rolling bearing failures. The first technique is based on the stator current analysis, the second one uses the vibration signal analysis and the last technique is devoted to the stray flux signature.The aim of the study is to highlight the performances of stray- flux technique in the detection of inner raceway fault comparatively to current and vibration. For this study, experimental tests are realized on a laboratory test bench allowing creation of artificial bearing damage. The analysis is focused on specific harmonics related to the electrical and mechanical frequencies

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

This paper presents the detailed detection of broken rotor bar faults in squirrel cage induction motors (SCIMs). This study used three diagnostic techniques. Early faults detection allows us to avoid catastrophic damage. In this work, we have exploited the stator current signal by three recent methods. The first technique uses the fast Fourier transform (FFT) which is often called motor current signature analysis (MCSA or MCSA-FFT). According to this technique, we carefully checked the spectral content of the stator current. In addition, we have clearly noticed to new harmonics that indicate the broken rotor bar (BRB) faults exists. The second technique is based on the discrete wavelet transform (DWT); this technique is widely used in the diagnosis field of rotating machinery. In order to detect the BRB faults in SCIMs, we have exploited this method by three important indicators. One of them is based on the mean square error (MSE) of each detail coefficient. In this study, we applied a new indicator (MSE) for the BRB fault detection based on DWT. The last method is empirical mode decomposition (EMD) to perform the current signature analysis in order to decompose the motor current signal into intrinsic mode functions (IMFs). It is currently competing with several methods such as: MCSA, DWT, etc. So, it is possible to detect BRBs through the evaluation of the different IMF levels for both conditions, healthy and faulty state of SCIM. An experimental test for different conditions: at no load or at load operation, healthy or faulty state of the induction motor has been performed. So, experimental results using three methods showed a detailed comparison between them in order to achieve a judicious decision on the broken rotor bars detection. Finally, we have confirmed the proposals ideas in this subject in order to detect the broken rotor bar faults

Evaluation of multi-user effects on the channel in the TH-UWB communication systems

This paper investigates the UWB channel to focus the light on the multi-user and their impact on the UWB communication performance. We addressed the performance evaluation of Time Hopping Ultra-Wideband (TH-UWB) system with the Pulse Position Modulation (PPM) by assuming multiuser scenarios. The channel used here is the IEEE 802.15.3a that consists of Channel Models (CM1~CM4). Moreover, a Rake receiver is adopted to collect the multipath occurring in the UWB channel. Thus, the reception of signals requires a channel estimation stage (for amplitudes and delays) which is achieved here using the Maximum-Likelihood (ML) approach with two contexts i.e. Data Added (DA) and Non-Data Added (NDA). Simulation results confirm that the channel estimation is affected slightly by the multi-user in terms of amplitudes and considerably in terms of delays, and thus leads to focus much on multipath time resolution to gain better UWB communication accuracy and BER performance

A Comparative Study between the adaptive wavelet transform and DWT Methods Applied to a Outer Raceway Fault Detection in Induction Motors based on the Frequencies Analysis

This paper presents a new application to diagnose the outer race fault in induction machines based on the three-step nonlinear lifting scheme. The wavelet transform is a powerful and complex tool in the context of diagnosis. The discovery of the lifting schemes structure make a wavelet filters simple, rapid and reversible. This method (lifting) is generally used by researchers in the field of image processing. However we are going in this study to use in order to see its effectiveness in the field of diagnosis in electrical machine in induction motors. In addition, we will exploit the experimental results. This study analyzes the stator current of an asynchronous motor for two conditions: the first is a data acquisition of a healthy machine. The second is a defective machine of an outer race fault and inner race fault. The objective of this work focuses particularly on the frequency analysis of the signal indicators of defects. Moreover, it remains necessary to develop filters for the detection, isolation and estimation of these defects by a certain number of diagnostic methods, techniques and to establish selection criteria for their use. For these reasons, this work compares the three-step nonlinear lifting scheme with the MCA-DWT current analysis method to access a valuable decision based on the experimental results

Novel detection algorithm of speech activity and the impact of speech codecs on remote speaker recognition system

International audienceIn this paper, we studied the effects of voice codecs on remote speaker recognition system, considering three types of speech codec: PCM, DPCM and ADPCM conforming to International Telecommunications Union - Telecoms (ITU-T) recommendation used in telephony and VoIP (Voice over Internet Protocol). To improve the performance of speaker recognition in a noisy environment, we propose a new speech activity detection algorithm (SAD) using "Adaptive Threshold", which can be simulated with speech wave files of TIMIT (Texas Instruments Massachusetts Institute of Technology) database that allows recognition system to be done under almost ideal conditions. Moreover, the speaker recognition system is based on Vector Quantization as speaker modeling technique and Mel Frequency Cepstral Coefficient (MFCC) as feature extraction technique. Where, the feature extraction proceed after (for testing phase) and before (for training phase) the speech is sending over communication channel. Therefore, the digital channels can introduce several types of degradation. To overcome the channel degradation, a convolutional code is used as error-control coding with AWGN channel. Finely, In our simulation with Matlab we have used 30 speakers of different regions (10 male and 20female), the best overall performance of speech codecs was observed for the PCM code in terms of recognition rate accuracy and runtim

A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process

International audienceIn bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression