Publications Scientifiques

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    Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
    (SAGE, 2021) Mahami, Amine; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
    In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.
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    Multi-fault bearing diagnosis under time-varying conditions using Empirical Wavelet Transform, Gaussian mixture model, and Random Forest classifier
    (SAGE Publications Inc., 2024) Imane, Moussaoui; Rahmoune, Chemseddine; Zair, Moahmed; Benazzouz, Djamel
    Bearing faults can cause heavy disruptions in machinery operation, which is why their reliable diagnosis is crucial. While current research into bearing fault analysis focuses on analyzing vibration data under constant working conditions, it is important to consider the challenges that arise when machinery runs at variable speeds, which is usually the case. This article proposes a multistage classifier for diagnosing bearings under time-variable conditions. We validate our method using vibration signals from five bearing health states, including a combined fault case. Our approach involves decomposing the signals using Empirical Wavelet Transform and computing temporal and frequency domain attributes. We use the Expectation-Maximization Gaussian mixture model for optimization concerns to identify relevant parameters and train the Random Forest classifier with the selected features. Our method, evaluated using the Polygon Area Metric, has demonstrated high effectiveness in diagnosing bearings under time-variable conditions. Our approach offers a promising solution that efficiently addresses speed variability and combined fault recognition issues.
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    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
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    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.
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    Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms
    (SAGE, 2024) Afia, Adel; Gougam, Fawzi; Rahmoune, Chemseddine; Touzout, Walid; Ouelmokhtar, Hand; Benazzouz, Djamel
    Due to their complexity and often harsh working environment, air compressors are inevitably exposed to a variety of faults and defects during their operation. Thus, condition monitoring is critically required for early fault recognition and detection to avoid any type industrial failures. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is developed using real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states such as leakage inlet valve (LIV), leakage outlet valve (LOV), non-return valve (NRV), piston ring, flywheel, rider-belt and bearing defects. The proposed algorithm mainly consists of three steps: feature extraction, selection, and classification. For feature extraction, experimental acoustic signals are decomposed using maximal overlap discrete wavelet packet transform (MODWPT) by six levels into 64 wavelet coefficients (nodes). Thereafter, time domain features are calculated for each node to build each air compressor’s health state feature matrix. Each feature matrix dimension is reduced by selecting the most useful features using Harris hawks optimization (HHO) in the feature selection step. Finally, for feature classification, selected features are used as inputs for random forest (RF), ensemble tree (ET) and K-nearest neighbors (KNN) to detect, identify, and classify the compressor health states with high classification accuracy. Comparative studies with several feature extraction and selection methods prove the proposed approach’s efficiency in detecting, identifying, and classifying all air compressor faults.
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    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.
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    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.
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    Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals
    (SAGE, 2023) Moussaoui, Imane; Rahmoune, Chemseddine; Benazzouz, Djamel
    The precise identification of faults is vital for ensuring the reliability of the bearing’s performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings’ health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions
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    Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier
    (SAGE Publications, 2021) Ikhlef, Boualem; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
    Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this paper, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency
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    Keratoconus prognosis study for patients with corneal external mechanical stress mode
    (Springer Nature, 2020) Bettahar, Toufik; Rahmoune, Chemseddine; Benazzouz, Djamel
    Purpose To demonstrate the correlation between excessive eye rubbing and corneal degeneration for Keratoconus patients. Materials and methods Keratoconus (KC) patients who regularly rub their eyes had shown a rapid degeneration rate of their affected corneas. This observation is experimentally and numerical discussed and developed based on clinical data of 8 of KC Patients with a mean age of 26.5 ± 9.4 years old, and four healthy individuals with a mean age of 24.33 ± 5 years old at the baseline. Corneal topography was used to measure both central corneal thickness (CCT) and its total refractive power. The registered data had been exploited to assess the progression of the disease, and the final results were embedded in a finite element model of human corneas to simulate their response to eye rubbing at different stages of the pathology. Corneal lifetime prognosis using multi-layer perceptron was then established to estimate the number of eye rubbing cycles for each stage of KC. Results The survey of KC patients who declared stopping eye rubbing had shown a decrease in CCT loss rate, followed by a durable stability. Mechanical stresses numerical simulations had shown different corneal behaviours in term of shape deformity, apical raise and corneal applanation between healthy and KC stages models. Apical rise ranged from 0.122 to 0.389 mm for an applied intraocular pressure that equals to 15 mmHg. A normal stress of 5 kPa provoked a corneal applanation that ranged from 0.27 mm in healthy cases to 1.173 mm in severe stages of the disease. The application of 2.5 kPa biaxial stress had resulted normal and tangential applanations that successively ranged from 0.152 and 0.173 mm in healthy corneas to 0.446 mm and 0.458 mm in severe KC stages. An adopted prognosis algorithm was able to predict the current stage of the disease and to estimate the remaining number of eye rubbing cycles before failure. Conclusion Eye rubbing was proven to be a considerable contributing factor in KC patient’s corneal degeneration. The progression of this pathology could be decreased or halted by stopping eye rubbing at early stages.