Browsing by Author "Rahmoune, Chemseddine"

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Analyse et traitement du courant statorique pour la détection des défauts dans les systèmes électromécaniques
(2011) Rahmoune, Chemseddine
L'objectif de ce travail de recherche est de modéliser et d'analyser le comportement d'un réducteur mécanique sain puis défaillant d'un moteur électrique a induction. Ce réducteur fait partie d'une charge mécanique couplée a un entrainement asynchrone, la finalité étant de détecter et d'analyser ses défauts a partir des courants d'alimentation de la machine qui l'entraine. La première partie est a caractère bibliographique: elle définit les différentes notions liées a la thématique Surveillance et Diagnostic des systèmes industriels, et établit un état de l'art sur les différentes approches utilisées pour leur détection. Une seconde partie consiste a modéliser et a simuler le fonctionnement sain des éléments du dispositif retenu dans l'environnement MATLAB. Un inventaire des défauts pouvant affecter le mécanisme de transmission est ensuite présente. Une modélisation des défauts envisages a l'étude est proposée. Une analyse du comportement du système électromécanique en présence des défauts est alors effectuée, en étudiant l'influence des défauts sur le courant statorique de la machine. La troisième partie présente l'outil de traitement du signal utilisé pour étudier le contenu spectral des formes d'onde qui résultent des simulations. En effet, l'analyse spectrale de l'information courant statorique est réalisée. Cette analyse mène a une caractérisation des défauts par des signatures spectrales issues du courant, et ce a partir d'une comparaison entre le fonctionnement sain du système, pris comme état de référence, et son fonctionnement en mode dégrade. Ceci permet de mettre en exergue l'intérêt de l'étude dans laquelle la détection des défauts mécaniques s'effectue uniquement a partir d'un traitement des grandeurs électriques
Automated transformer fault diagnosis using infrared thermography imaging, GIST and machine learning technique
(SAGE, 2022) Mahami, Amine; Rahmoune, Chemseddine; Benazzouz, Djamel
Condition monitoring of electrical systems is vital in reducing maintenance costs and enhancing their reliability. By focusing on the monitoring of electrical transformers, which play a crucial role in electrical systems and are the main equipment for electrical transmission and distribution, drastic damages, undesirable loss of power and expensive curative maintenance could be avoided. In this paper, a novel noncontact and non-intrusive framework experimental method is used for the monitoring and the diagnosis of transformer faults based on an infrared thermography technique (IRT). The basic structure of this work begins with applying (IRT) to obtain a thermograph of the considered machine. Second, GIST features of the reference image and all images in the image database are extracted. At last, various faults patterns in the transformer are automatically identified using a machine learning method called Support Vector Machine (SVM). The proposed method effectiveness and capacity are evaluated based on the experimental infrared thermography (IRT) images and the diagnosis results by identifying nine sorts of electrical transformer states among which one is healthy and the remaining eight are of short circuit faults in common core winding type, and showing that it can be considered as a powerful diagnostic tool with high Classification Accuracy (CA) and stability compared to other previously used methods
Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network
(JVE International, 2019) Zair, Mohamed; Rahmoune, Chemseddine; Benazzouz, Djamel; Ratni, Azeddine
Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault. © 2019 Zair Mohamed, et al
Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network
(2019) Zair, Mohamed; Rahmoune, Chemseddine; Benazzouz, Djamel; Ratni, Azeddine
Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault
Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier
(SAGE Publications, 2021) Moussaoui, Imane; Rahmoune, Chemseddine; Zair, Mohamed; Benazzouz, Djamel
Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency
Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions
(JVE International, 2019) Gougam, Fawzi; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem
Condition monitoring of rotating machines has become a more important strategy in structural health monitoring (SHM) research. For fault recognition, the analysis is categorized in two essential main parts: Feature extraction and classification; the first one is used for extracting the information from the signal and the other for decision-making based on these features. A higher accuracy is needed for sensitive places to avoid all kinds of damages that can lead to economic losses and it may affect the human safety as well. In this paper, we propose a new hybrid and automatic approach for bearing faults diagnosis. This method uses a combination between Empirical wavelet Transform (EWT) and Fuzzy logic System (FLS), in order to detect and localize the early degradation of bearing state under different working conditions. EWT build a wavelet filter bank to extract amplitude modulated-frequency modulated component of signal. Modes presenting a high impulsiveness is then selected using the kurtosis indicator. Thereafter, time domain features (TDFs) are applied for the reconstructed signal to extract the fault features which are finally used as an inputs of FLS in order to identify and classify the bearing states. The experimental results shows that the proposed method can accurately extract and classify the bearing fault under variable conditions. Moreover, performance of EWT and empirical mode decomposition (EMD) are studied and shows the superiority of the proposed method
Bearing faults classification using a new approach of signal processing combined with machine learning algorithms
(Springer Nature, 2024) Gougam, Fawzi; Afia, Adel; Soualhi, Abdenour; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
Vibration analysis plays a crucial role in fault and abnormality diagnosis in various mechanical systems. However, efficient vibration signal processing is required for valuable diagnosis and hidden patterns’ detection and identification. Hence, the present paper explores the application of a robust signal processing method called maximal overlap discrete wavelet packet transform (MODWPT) that supports multiresolution analysis, allowing for the examination of signal details at different scales. This capability is valuable for identifying faults that may manifest at different frequency ranges. MODWPT is combined with covariance and eigenvalues to signal reconstruction. After that, health indicators are specifically applied on the reconstructed vibration signal for feature extraction. The proposed approach was carried out on an experimental test rig where the obtained results demonstrate its effectiveness through confusion matrix analysis of machine learning tools. The ensemble tree model gives more accurate results (accuracy and stability) of bearing faults classification and efficiently identify potential failures and anomalies in mechanical equipment.
Computer numerical control machine tool wear monitoring through a data-driven approach
(SAGE, 2024) Gougam, Fawzi; Afia, Adel; Ait Chikh, Mohamed Abdessamed; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods
Contribution à la Surveillance des Systèmes Electromécaniques
(2015) Rahmoune, Chemseddine
Les méthodes classiques (t-q l'analyse temporelle et l'analyse fréquentielle) peuvent être systématiquement utilisées pour révéler des informations sur l'état du système à partir de l'analyse du courant statorique. Au cours de ses dernières années, l'analyse en ondelettes, qui peut conduire à une identification claire de la nature des défauts, est largement utilisée pour décrire l'état du système. La capacité de cette méthode à la détection d'une anomalie peut être encore améliorée lorsque les moments de fréquence d'ordre inférieur sont considérés. Cette thèse présente l'utilisation du Kurtogram rapide pour la détection précoce des défauts du réducteur. A cet effet, un modèle dynamique d'un système électromécanique qui un réducteur de vitesse (avec et sans défaut pignon) entraîné par une machine asynchrone triphasé est développé. Ensuite, le courant statorique du moteur est analysé en utilisant le Kurtogramme rapide
Detection of shaft crack fault in gearbox using hilbert transforms
(IEEE, 2017) Ratni, Azeddine; Rahmoune, Chemseddine; Benazzouz, Djamel; Ould bouamama, Belkacem; Merainani, Boualem
Early detection of pitting failure in gears using a spectral kurtosis analysis
(2012) Rahmoune, Chemseddine; Benazzouz, Djamel
Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform
(Sage, 2017) Merainani, Boualem; Benazzouz, Djamel; Rahmoune, Chemseddine
An early gear fault diagnosis method based on rlmd, hilbert transform and cepstrum analysis
(Acta Press, 2021) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel
Gear fault diagnosis requires an adaptive decomposition method to extract defect signature. As a self-adaptive approach, local mean decomposition (LMD) decomposes the signal to a set of product functions (PFs). However, LMD suﬀers from two limits: mode mixing and end eﬀect. To overcome this problem, an optimized technique named “robust LMD (RLMD) uses an integrated frame- work: a mirror extending method to ﬁnd the real extrema in data as well as a self-adaptive tool to select the size of the ﬁxed sub- set for the moving average algorithm for the envelope estimation and ﬁnally, a soft sifting stopping criterion to automatically stop the sifting process after determining the most optimum number of sifting iterations. In this article, a combination between RLMD, Hilbert transform (HT), kurtosis and cepstrum analysis is made to monitor a gearbox with chipped tooth using experimental signals. Data are ﬁrst decomposed using RLMD into a couple of PFs, then HT is applied to each PF to get the envelope for every decom- posed component and highlights the modulated signal related to the gear fault. Subsequently, kurtosis is applied to each envelope to obtain the kurtosis vector for each signal. As healthy vibration characteristics are always taken as a reference, in this article every faulty kurtosis vector is subtracted from the healthy vector, and the PF with the largest kurtosis diﬀerence will be selected. Finally, cepstrum analysis is applied to the selected PF to extract the fault signature. Results indicate that our method can detect the chipped tooth in an earlier stage even in a noisy environment
Enhancing air compressors multi fault classification using new criteria for Harris Hawks optimization algorithm in tandem with MODWPT and LSSVM classifier
(SAGE, 2023) Rahmoune, Chemseddine; Amine Sahraoui, Mohammed; Gougam, Fawzi; Zair, Mohamed; Meddour, Ikhlas
The evolution of industrial systems toward Industry 4.0 presents the challenge of developing robust and accurate models. In this context, feature selection plays a pivotal role in refining machine learning models. This paper addresses the imperative of accurate fault diagnosis in industrial systems, focusing on air compressors. These systems, vital for efficient operations, demand early fault detection to prevent performance degradation. Conventional methods often encounter challenges due to the occurrence of similar failure patterns under comparable conditions. To address this limitation, our approach delves into a more complex scenario, where air compressors operate under diverse fault conditions. This study introduces novel feature selection criteria achieved through a fusion of the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT), the Harris Hawks Optimization (HHO) algorithm, and the Least Squares Support Vector Machine (LSSVM) classifier. The synthesis of these components aims to bolster the multi-fault diagnosis accuracy and stability for each fault class. The evaluation focuses on key statistical metrics—minimum, maximum, mean, and standard deviation. Experimental outcomes underscore the method’s superiority over traditional feature selection techniques. The approach excels in accuracy and stability, particularly across various fault categories, affirming the efficacy and resilience of the new criteria. The symbiotic integration of MODWPT, HHO, and LSSVM within our framework highlights its potential to elevate classification performance in the realm of industrial fault diagnosis.
Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy
(SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Zair, Mohamed; Gougam, Fawzi; Damou, Ali
Petroleum serves as a cornerstone of global energy supply, underpinning economic development. Consequently, the effective detection of faults in oil and gas (O&G) wells is of paramount importance. In response to the limitations observed in prior research, this study presents an innovative fault diagnosis system, rooted in machine learning techniques. Our approach encompasses a comprehensive analysis, incorporating stability assessment via standard deviation (STD), and a meticulous evaluation of accuracy and stability for distinct fault scenarios. By integrating data preprocessing, feature selection methods, and deploying a robust random forest classifier, our model achieves a substantial enhancement in fault classification accuracy and stability. Extensive experimentation substantiates the superiority of our approach, surpassing the performance of previous studies that predominantly emphasized overall accuracy while disregarding stability analysis. Notably, our model attains remarkable accuracies, notably achieving a flawless 100% accuracy for scenario 3 faults. Detailed examination of mean accuracies and STDs further reinforces the precision and consistency of our model's predictive capabilities. Additionally, a qualitative assessment underscores the practical utility and reliability of our model in accurately identifying critical fault types. This research significantly advances fault detection methodologies within the O&G industry, providing valuable insights for decision-making systems in oil well operations.
Fault feature extraction and classification based on HEWT and SVD : application to rolling bearings under variable conditions
(IEEE, 2017) Merainani, Boualem; Rahmoune, Chemseddine; Benazzouz, Djamel; Ould-Bouamama, Belkacem; Ratni, Azeddine
Gear fault detection, identification and classification using MLP neural network
(Springer, 2023) Afia, Adel; Ouelmokhtar, Hand; Gougam, Fawzi; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
Gear fault detection, identification and classification are highly complicated tasks, as the faults which affect gearboxes tend to share similar frequency signatures. Therefore, load and speed changes in a rotating machinery inevitably provide inaccurate results. However, identifying the fault remains critical, as each individual gear fault influences overall mechanism operation in different manners. Therefore, defect identification and classification appear as the hardest challenge for a geared systems. An automatic method to detect, identify and classify different gear failures is presented in this paper. The intelligent approach consists of a combination of MODWPT, entropy and MLPNN. MODWPT was developed to decompose the signals with a uniform frequency bandwidth. Entropy is employed to build the feature matrix in the feature extraction phase. Then, MLP offers a very efficient classification tool for features classification stage. Based on data sets taken from a gearbox bench test with a good and five varied gear states under various loads and speeds, experimental results presented the efficiency of our technique
Gear fault diagnosis using Autogram analysis
(Sage, 2018) Afia, Adel; Rahmoune, Chemseddine; Djamel, Benazzouz
Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness
Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform
(JVE International, 2018) Rahmoune, Chemseddine; Merainani, Boualem; Benazzouz, Djamel; Fedala, Semchedine
Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction
Gear Multi-Fault Feature Extraction and Classification Based on Fuzzy Entropy of Local Mean Decomposition, Singular Value Decomposition and MLP Neural Network
(2018) Zair, Mohamed; Rahmoune, Chemseddine; Benazzouz, Djamel
The condition monitoring and fault diagnosis of gears is a very important in industrial machinery. In this paper, we propose a new method, by combining the fuzzy entropy of LMD-SVD and Multilayer Perceptron (MLP) neural network to overcome the problem of identification and classification faults in gearbox system. The LMD process allows the vibration signal to decompose into series of Product functions (PF). The result obtained from fuzzyEn of LMD are defined as the input vectors of the SVD. This SVD is used to reduce the dimension of the feature vectors. Last, the reduced feature vectors are chosen as input of MLP network for fault diagnosis and fault classification. The obtained results through experimental results, show that the proposed method can accurately extract and classify the gear fault features.