Publications Internationales
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Item Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques(SAGE, 2021) Mahami, Amine; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, DjamelIn 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.Item 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, DjamelBearing 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.Item Intelligent multi-fault identification and classification of defective bearings in gearbox(SAGE Publications Inc., 2024) Damou, Ali; Ratni, Azeddine; Benazzouz, DjamelBearing faults in gearbox systems pose critical challenges to industrial operations, needing advanced diagnostic techniques for timely and accurate identification. In this paper, we propose a new hybrid method for automated classification and identification of defective bearings in gearbox systems with identical rotating frequencies. The method successfully segmented the signals and captured specific frequency components for deeper analysis employing three distinct signal processing approaches, ensemble empirical mode decomposition EEMD, wavelet packet transform WPT, empirical wavelet transform EWT. By decomposing vibration signals into discrete frequency bands using WPT, relevant features were extracted from each sub-band in the time domain, enabling the capturing of distinct fault characteristics across various frequency ranges. This extensive set of features is then served as inputs for machine learning algorithm in order to identify and classify the defective bearing in the gearbox system. Random forest RF, decision tree DT, ensemble tree ET classifiers showcased a notable accuracy in classifying different fault types and their localizations. The new approach shows the high performance of the diagnostic gearbox with a minimum of accuracy (Min = 99.95 %) and higher stability (standard deviation = 0.1).Item 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, DjamelThe 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) methodsItem CNC milling cutters condition monitoring based on empirical wavelet packet decomposition(Springer Nature, 2023) Amar Bouzid, Abir; Merainani, Boualem; Benazzouz, DjamelMachining is a versatile field in the manufacturing industry. In milling operations, tool wear is considered the most critical factor affecting the surface quality of the milled piece. Furthermore, the gradual tool wear impacts the milling process, leading to significant downtime, which has serious financial consequences. Unavoidably, a sustainable and reliable condition monitoring system must be developed to reduce the risk of downtime and enhance production quality. The deployment of prognostic and health management (PHM) solutions is becoming increasingly important. It is regarded as one of the main levers for monitoring tool wear status. In this paper, a novel methodology is proposed for extracting pertinent health indicators (HIs) that reflect the degradation behavior of a set of milling cutters and estimating their remaining useful lives (RULs). First, a new time-frequency signal-analysis approach, titled empirical wavelet packet decomposition (EWPD), is proposed to scrutinize the data collected via multi-sensor acquisition. This technique provides a new segmentation of the signal’s Fourier spectrum, distributed on levels, to investigate a broader variety of frequency bands and enhance the traditional segmentation structure’s performance. Second, a new health indicator is designed based on an innovative selection of the time-domain features computed for each frequency band over each level. Finally, the long short-term memory (LSTM) network is used to estimate the RUL of each cutter. A comparison between the suggested processing method and the wavelet packet transform (WPT) is made to support the hypothesis regarding the effectiveness of the proposed technique. Experimental outcomes seem to be satisfying.Item 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, DjamelDue 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.Item 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, DjamelVibration 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.Item Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals(SAGE, 2023) Moussaoui, Imane; Rahmoune, Chemseddine; Benazzouz, DjamelThe 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 conditionsItem Automated transformer fault diagnosis using infrared thermography imaging, GIST and machine learning technique(SAGE, 2022) Mahami, Amine; Rahmoune, Chemseddine; Benazzouz, DjamelCondition 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 methodsItem Near-Optimal covering solution for USV coastal monitoring using PAES(Springer, 2022) Ouelmokhtar, Hand; Benmoussa, Yahia; Diguet, Jean-Philippe; Benazzouz, Djamel; Lemarchand, LaurentThis paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one