Publications Scientifiques
Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10
Browse
9 results
Search Results
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 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 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, DjamelElectrical 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 problematicsItem 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 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, DjamelBearings 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 efficiencyItem 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, DjamelGearboxes 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 efficiencyItem 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, SemchedineVibration 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 extractionItem 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