Publications Scientifiques

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Author: search.filters.author.Benazzouz, Djamel

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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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    Intelligent multi-fault identification and classification of defective bearings in gearbox
    (SAGE Publications Inc., 2024) Damou, Ali; Ratni, Azeddine; Benazzouz, Djamel
    Bearing 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).
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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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    CNC milling cutters condition monitoring based on empirical wavelet packet decomposition
    (Springer Nature, 2023) Amar Bouzid, Abir; Merainani, Boualem; Benazzouz, Djamel
    Machining 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.
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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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    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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    A new transformer condition monitoring based on infrared thermography imaging and machine learning
    (Springer, 2023) Mahami, Amine; Bettahar, Toufik; Rahmoune, Chemseddine; Amrane, Foudil; Touati, Mohamed; Benazzouz, Djamel
    Electrical systems maintenance is becoming a crucial and an important part in the economic policies and that’s due their deep implication in the majority of the industrial installations. Electrical transmission and distribution relay mainly on transformers. Electrical transformers condition monitoring plays a major role in increasing their availability, enhancing their reliability and preventing further major failures and high cost maintenance. A new non-contact and non-intrusive method is adopted in this paper to monitor electrical transformers and diagnose their faults based on infrared thermography imaging techniques (IRT). When thermographs are obtained using an infrared camera for different states of the studied transformer, a dataset is then prepared for the following step. Features extraction was applied on the considered infrared images to be used later as input indicators for an automatic classification and identification of transformer’s healthy and several faulty states based machine learning methods (LS-SVM). This method was applied and compared with several IA techniques in order to select the most efficient one in term of accuracy and stability to be relied on in this purpose. The proposed technique, which is mainly based on IRT, features extraction and machine learning, has shown a remarkable efficiency in transformers condition monitoring and an accurate faults diagnosis, and can be generalized as a reliable and powerful tool in such problematics
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    Edge detection of MRI brain images based on segmentation and classification using support vector machines and neural networks pattern recognition
    (Springer, 2023) Iourzikene, Zouhir; Benazzouz, Djamel; Gougam, Fawzi
    Brain tumor (brain cancer) is a mass of abnormal cells that grow in the brain in an uncontrolled way. Brain CT and brain MRI are the most frequently performed examinations. The objective of this paper is to develop a method for the classification of brain MRI images of healthy cases and tumor cases. MRI brain database is obtained by preprocessing, segmentation, feature extraction. Feature extraction based on support vector machines (clustering) is used in this research. The objective of this method is to create several vectors and each vector contains a number of features of each image, so that we can make the classification by these features