ABDERRZAK Laib

CHALABI Chames , DJOUADI Ikram

DIAGNOSTIC INTELLIGENT DES ROULEMENTS DANS LES MACHINES ELECTRIQUES PAR FORET ALEATOIRE

Aslafi Ahmed , Hammoudi salaheddine, Elassri yassin

DDétection et localisation des défauts dans les Réseaux électriques filaires par la réflectométrie et ANFIS

AZAZ Ayat Errahmen , BENDJEDDOU Omayma

Détection et localisation des défauts non francs dans les réseaux à base de câbles coaxiaux par la réflectométrie et la corrélation soustractive

GUIMEUR Loubna , HADJ BRAHIM Nawel

Détection et localisation des défauts non francs dans les réseaux filaires embarqués par la réflectométrie et les ondelettes

GASMI Sohaib

Détection et localisation des défauts dans les réseaux électriques filaires par la réflectométrie et les réseaux de neurones

CHENNI Ahmed Badis , CHARIF Abderrazak

Analyse et synthèse d’un signal de parole par la Matrice de Pencil en vue d’une discrimination de locuteurs.

Rolling element bearings are essential components in heavy-duty machinery such as drilling rigs, mining equipment, wind turbine rotors, and helicopter swash plates. Their failure can cause costly downtime and major repairs, making early fault detection and continuous monitoring critical. Traditional vibration-based methods, although widely applied, often struggle to identify incipient faults under variable operating conditions. Acoustic Emission (AE) sensing has emerged as a promising alternative, offering high sensitivity to microscopic damage and transient events. However, many AE-based diagnostic approaches rely on characteristic fault frequency extraction, which requires precise information about rotational speed and bearing geometry, limiting their practicality. This study introduces an AE-based fault localization method that combines wavelet-derived features with a Random Forest classifier. Experiments on the UORED-AFCLS dataset show that energy-based features achieve near-perfect classification accuracy across different fault types. The results highlight AE’s potential, when integrated with machine learning, to provide an effective and reliable framework for early fault detection in complex industrial environments.

Citation

Abderrzak Laib , ,(2025-12-10), Acoustic Emission Based Bearing Fault Localization Using Wavelet Features and Random Forest Classifier,The 2nd International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE2025),Msila, Algeria

This research enhances the dynamic performance and robustness of Permanent Magnet Synchronous Motors (PMSMs) by developing an adaptive fuzzy-PI hybrid control strategy. While PMSMs offer superior power density and durability over DC motors, their control is challenged by parameter variations and external disturbances. This study compares traditional Proportional-Integral (PI) control, standard fuzzy logic, and a proposed adaptive fuzzy controller. Numerical simulations in MATLAB/Simulink demonstrate that the adaptive fuzzy controller significantly improves transient response by minimizing speed overshoot and effectively rejecting disturbances. Upon application of a 6 N.m load torque, the controller rapidly restores the motor speed to its reference value with minimal deviation. Furthermore, it substantially reduces current and torque peaks during transients, thereby decreasing stress on the motor and drive system. The results confirm that the adaptive fuzzy-PI control optimizes dynamic responsiveness, ensures stability, and enhances overall control efficiency for PMSMs in demanding industrial environments.

Citation

Abderrzak Laib , ,(2025-12-10), Adaptive Fuzzy-PI Hybrid Control of Permanent Magnet Synchronous Motors,2nd International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE) 2025,Msila, Algeria

Vibration signal analysis plays a vital role in the condition‐based preventive maintenance of induction motor by identifying early signs of motor issues, avoiding costly breakdowns and optimising the motor's maintenance schedule. It provides detailed information very useful for extending the motor's life cycle with proactive, condition‐specific maintenance. Furthermore, the vibration signal analysis offers the advantage of identifying the health status of rotating machinery as a whole, as well as its individual components. This paper presents an innovative solution for the automated health assessment of a critical induction motor component: the bearing. Our approach uses the matrix pencil method for signal processing and health signature gen- eration, combined with a multilayer perceptron neural network to detect health conditions from the resulting health signature characteristics. Initially, the matrix pencil is applied to the vibration signal to identify the mean frequency characteristics. This vector provides a holistic view of the signal’s inherent features and transforms its frequency characteristics into a visual spectrum, resulting in improved induction motor bearing fault condition monitoring. Subsequently, the output from the matrix pencil mean frequency analysis is processed by a multilayer perceptron neural classifier, chosen for its low computational cost and high classification accuracy. Experimental validation demonstrates a 100% fault classification rate and automatic identi- fication of defective components. Comprehensive validation further confirms the method’s robustness and feasibility for in- duction motor bearing fault detection compared to other recently methods.

Citation

Abderrzak Laib , , (2025-11-08), Enhanced Automated Condition Assessment of Induction Motor Bearings: A Novel Approach Using Matrix Pencil Mean Frequency Signal Processing and Multilayer Perceptron Neural Networks, IET Electric Power Applications, Vol:19, Issue:1, pages:1-25, Wiley

The present paper sets forth the implementation of an advanced control methodology, designated as Iterative Feedback Tuning (IFT), for the purpose of enhancing the performance of an asynchronous (induction) motor. IFT is a data-driven technique that allows controller parameters to be tuned directly from closed-loop experiments, thus obviating the necessity for a mathematical model of the system. This renders it especially useful for systems where modelling is difficult, time-consuming, or prone to inaccuracies. The proposed approach involves the real-time collection of system response data during operational processes. Subsequently, this data is utilised to facilitate the refinement of controller parameters through the implementation of a gradient-based algorithm. The objective is to minimise a cost function that represents the discrepancy between the desired and actual motor performance. By repeating this process iteratively, the controller gradually improves and adapts to the system's dynamics. The method was applied to an asynchronous motor, which is widely used in industrial applications due to its robustness and simplicity. The findings indicate that IFT can enhance the motor's speed and torque response under diverse operating conditions, obviating the necessity for prior knowledge of the motor's internal model. The present study corroborates the practicality and efficiency of Iterative Feedback Tuning for real-time control systems, particularly in electrical drives.

Citation

Abderrzak Laib , ,(2025-08-10), Application of Iterative Feedback Tuning to the Control of an Asynchronous Motor,6th International Conference on Engineering, Natural and Social Sciences,Turkey

L’objectif de cours est de traiter les fonctions et l'architecture informatique des centres de conduite des réseaux de transport et de distribution de l'énergie électrique : rôle des centres de conduite; aspects temps réel; architecture; acquisition de données et télécommande; estimation et prédiction de l'état du réseau; réglages centralisés; optimisation; fiabilité et sécurité; échanges d'informations entre applications et entre centres de conduite.

Citation

AbderrzakLaib , ,(2025-04-29); Conduite Des Réseaux Electriques,Msila university,

The transmission lines and the THT "very high voltage" and high voltage HTB "High voltage type B" electrical power stations constitute an essential part of an electrical network that must ensure the continuity of the electricity supply to HTA "High Voltage type A" and LV "Base Voltage" consumers. This is not always the case, because these lines are often exposed to incidents or faults that can interrupt this service and cause significant financial losses for manufacturers and inconvenience for ordinary consumers. Since the entry of programmable digital relays on the market in the last fifteen years, for electrical protection, several algorithms have been developed to make these relays more efficient both in terms of their operating speed and their accuracy. Our work consists of a complete study on the 330 KV transmission network to the high voltage electrical networks of Ghardaïa, by injecting several types of faults in order to deduce the degree of performance and the reliability of the setting thresholds of the protection relays.

Citation

Abderrzak Laib , ,(2025-03-04), Protection Systems of the Electricity Transmission Network of the 220kv Ghardaïa - Ouargla Line,4th International Conference on Recent Academic Studies,Turkey

The life of companies in industrialized countries or those in the process of becoming industrialized is characterized by profound changes: technological, economic and social. Eliminating certain types of jobs, these changes pose obvious social problems: you only have to open your usual newspaper to realize this. But they also create new needs that we must identify, then satisfy in terms of training and qualification of the actors, management and especially organization. Maintenance is one of the functions of the company, but it is not an end in itself, it is visible and sometimes unknown to decision-makers who underestimate its impact. And yet, it is becoming an increasingly sensitive component of the company's performance. It is therefore important to make it better known. The project's interests revolve around industrial maintenance, with an emphasis on the study and evaluation of the industrial maintenance system within companies. More specifically, the project studies the maintenance system in the SKTM company – Tamanrasset province.

Citation

Abderrzak Laib , ,(2025-02-11), Study and Evaluation of the Maintenance System of the SKTMTamanrasset Company,2nd International Conference on Recent and Innovative Results in Engineering and Technology,Turkey

Maintenance is one of the constraints encountered by any operator of an industrial facility. More generally, a production facility requiring a set of material and human resources is only able to provide the service requested of it after overcoming various constraints, including the maintenance of the production equipment used. Today, controlling the availability of goods, materials and industrial equipment would allow the industry to act on the regularity of its production, its manufacturing costs, its competitiveness and its commercial success. To sell more, to sell better, it is now no longer just a question of offering a better method of operating the facility but of guaranteeing the operator a rapid intervention method, a detection and diagnosis of failures, in a word, quality maintenance to achieve optimum production. Our project is based on industrial maintenance, from the point of view of the proportion of the application of maintenance within the ALFAPIPE-Ghardaïa company. The project answers the question: does the ALFAPIPE company apply the instructions and maintenance planning that we study and that exists in the documentation or not?. This work is organized into two main parts that can be summarized as follows: Part 1, is devoted to reminders of the maintenance function and tries to introduce the fundamental notions of this field. It gives the Concepts, objectives and policies of maintenance. In the second part, we made a practical study on maintenance within the ALFAPIPE-Ghardaïa Company.

Citation

Abderrzak Laib , ,(2025-01-30), Study and evaluation of the maintenance system of the ALFAPIPEGhardaïa company,2nd International Conference on Pioneer and Innovative Studies,Turkey

Time-Domain Reflectometry (TDR) generally consists of injecting a signal into the Network Under Test (NUT), and then collecting the multiple reflections that occur from each instance of junction or termination. However, since reflectometers combine primary reflections with multiple and intermediate reflections, pulses in the reflectometry response often overlap. Therefore, reconstructing wiring networks using only TDR responses is not feasible. In this paper, the Wiring Network Reconstruction (WNR) process is formulated as an optimisation problem. The optimisation algorithm used in this paper to solve the formulated problem is the Forensic-Based Investigation (FBI) algorithm. Solving the WNR problem consists of finding the topology and the length of branches of the NUT. In the objective function, the optimisation algorithm compares the TDR obtained from the NUT with the TDR of the predicted solutions, where all TDRs are generated analytically to save time. The obtained results using the FBI algorithm are tested against ten well-known optimisation algorithms over a set of seven experiments. In these experiments, five cases are simulation-based, while the last two are real-world cases. The results provided in the paper clearly show the effectiveness and resilience of the proposed approach for reconstructing wiring networks with different degrees of complexity.

Citation

Abderrzak Laib , , (2025-01-07), Topology reconstruction of wiring networks using an iterative process based on Time-Domain Reflectometry and Forensic-Based Investigation algorithm, Nondestructive Testing and Evaluation, Vol:40, Issue:11, pages:5607–5639, taylor and francis

The paper’s primary focus is on the monitoring of vibration signals and introduces an innovative method for the detection of bearing faults in electric machines WTMP. While conventional techniques based on vibration signals are popular in identifying the characteristic frequencies associated with faults, they encounter difficulties when dealing with signals that vary over time (non-stationary signals). To tackle this challenge, the proposed approach combines three distinct techniques: Continuous Wavelet Transform (CWT), Wavelet Packet Transform (WPT), and Matrix Pencil (MP). This hybrid method has several objectives: It aims to reconstruct signals that exhibit non-stationary behavior, emphasize the frequency related to bearing faults, and ultimately enhance the accuracy of fault detection. By harnessing the unique strengths of CWT, WPT, and MP, this proposed approach significantly improves the effectiveness of condition monitoring in electric machines, particularly in the context of detecting bearing faults. To validate the method’s performance, an experimental setup has been established. This setup allows for testing under various load conditions, offering a comprehensive assessment of the capabilities of the proposed technique. This rigorous experimental testing ensures the method's reliability and practical applicability in real-world scenarios.

Citation

Abderrzak Laib , , (2024-07-06), Real-time detection of bearing faults through a hybrid WTMP analysis of frequency-related states, International Journal of Dynamics and Control, Vol:12, Issue:, pages:3947–3962, Springer International Publishing

electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO-LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real-world systems.

Citation

Abderrzak Laib , , (2024-03-07), Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine, IET Science, Measurement & Technolog, Vol:18, Issue:8, pages:417-428, iet

Non-stationary fault detection under bearing fault operation of induction motor is investigated in this paper. For this aim, the vibration signal is analyzed by wavelet method and pencil matrix method. The pencil matrix (PM) or (MP) method has been combined with wavelet transform (WT), in order to reconstruct the non-stationary signal and detect the bearing fault frequency. For validation of results, an experimental setup is used for an induction motor under different load operation and with failure on its inner race. The application of the proposed technique on vibration signal under non-stationary state show that fault can be characterized by a particular signature that it is not possible with fast Fourier transform (FFT).

Citation

Abderrzak Laib , , (2024-01-29), Frequency bearing fault detection in non-stationary state operation of induction motors using hybrid approach based on wavelet transforms and pencil matrix, Electrical Engineering, Vol:106, Issue:, pages:4397–4413, Springer Berlin Heidelberg

In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios

Citation

Abderrzak Laib , , (2023-11-07), Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids, Engineering applications of artificial intelligence, Vol:127, Issue:, pages:B, Sciencedirect

Authorship Attribution is a research area concerned with the automatic classification of text documents based on their authors. The main objective of this investigation area is to find out who is the author of a given text. This task becomes very tough as far as old text documents are concerned. In this paper, we attempted to broach the Authorship Attribution problem as it applies to old text documents. For this purpose, several experiments are conducted and their results are commented. In order to validate the performances of our system, we constructed a special dataset that we called ''A10P '' (10 Ancient Arabic Philosophers), by quoting texts from the works of 10 ancient Arabic philosophers, where the topic of the different texts is the same. Moreover, the genre of the authors is also the same.

Citation

Abderrzak Laib , ,(2022), Performances Evaluation of Automatic Authorship Attribution on Ancient Arabic Documents,International Conference on advanced Technology in Electronic and Electrical Engineering (ICATEEE2022),Msila, Algeria

Authorship Attribution is a research area concerned with the automatic classification of text documents based on their authors. The main objective of this investigation area is to find out who is the author of a given text. This task becomes very tough as far as old text documents are concerned. In this paper, we attempted to broach the Authorship Attribution problem as it applies to old text documents. For this purpose, several experiments are conducted and their results are commented. In order to validate the performances of our system, we constructed a special dataset that we called ''A10P '' (10 Ancient Arabic Philosophers), by quoting texts from the works of 10 ancient Arabic philosophers, where the topic of the different texts is the same. Moreover, the genre of the authors is also the same.

Citation

Salah KHENNOUF , Abderrzak Laib , Halim Sayoud, ,(2022), Performances Evaluation of Automatic Authorship Attribution on Ancient Arabic Documents,International Conference of advanced Technology in Electronic and Electrical Engineering,University of Msila

Modern maritime microgrid systems are witnessing a revolutionary advancement by integrating more renewable energy sources and energy storage systems. The integration of these sophisticated systems is achieved, however, through the power electronics converters that cause severe harmonic contamination. This problem becomes more serious when some of these harmonics that are non-integer multiples of the fundamental (inter-harmonics) exist concurrently with both system frequency drifts and large-power transients, which is a commune issue in maritime microgrid systems such as shipboard microgrids. Hence, the performance of the widely signal processing algorithms applied in the measurement and communication systems such as the smart meters and power quality analyzers tends to worsen. To address this problem this paper proposes an effective method based on the eigenvalue solution to estimate the harmonics and inter-harmonics of modern maritime microgrid systems effectively. This method, which is a system frequency independent technique can work effectively even under large frequency drifts with short window width. The proposed method is evaluated under MATLAB software, and then the experimental validation is carried out via analyzing the electrical power system current of a bulk carrier ship.

Citation

Abderrzak Laib , , (2021), A Frequency Independent Technique to Estimate Harmonics and Interharmonics in Shipboard Microgrids, IEEE Transactions on Smart Grid, Vol:13, Issue:2, pages:888-899, IEEE

Due to the rapid adoption of power electronics technology in shipboard microgrids (SMGs) in recent years, harmonic contamination has become now a crucial topic for these power systems. In order to sustain the safety of the electrical power systems, standards for power quality have imposed strict limits on the harmonic distortion allowed. In these standards, the application of the fast Fourier transform (FFT) with a window size of 10/12 cycles is often recommended for the harmonic evaluation. This method is not practical for SMGs due to large variations in load and frequency in a short duration. To address this issue, this article proposes a signal periodicity-independent algorithm to estimate the current harmonic distortion of SMGs by solving an eigenvalue problem with a short transient response. The proposed algorithm, which is based on a resolution-enhanced sliding matrix pencil method (SMPM), is distinguished by its frequency independency feature, and as a result of this, feature system frequency variations and the existence of interharmonics do not affect its accuracy. The evaluation of the proposed method is carried out under the MATLAB software and is experimentally verified via analyzing the electrical power system current of a container ship

Citation

Abderrzak Laib , , (2020), A resolution-enhanced sliding matrix pencil method for evaluation of harmonics distortion in shipboard microgrids, IEEE Transactions on Transportation Electrification, Vol:6, Issue:3, pages:1290-1300, IEEE

To suppress the most dominant harmonic currents and compensate for the power factor (PF) of electric generators (EGs), a technique based on passive power filters (PPFs) is presented here. The key feature of the proposed method is the implementation of only a set of PPFs for compensating the power-quality issues of two EGs. This characteristic makes the suggested approach highly cost-effective and considerably reduces the required space for placing PPFs. The performance of the proposed technique is demonstrated using simulation studies under MATLAB/Simulink environment and validated experimentally

Citation

Abderrzak Laib , , (2019), Effective and low‐cost passive compensator system to improve the power quality of two electric generators, IET Power Electronics, Vol:12, Issue:7, pages:1833-1840, The Institution of Engineering and Technology

the main purpose of this work is to implement a new and accurate approach based on Time Domain Reflectometry (TDR) combined with Adaptive Neuro-Fuzzy Inference System (ANFIS) to solve the problem of soft faults detection and localization on complex wiring electric network. Firstly, the response of the transmission line is given by applying the Finite Difference Time Domain method (FDTD) on the transmission line equations. Then, the ANFIS method is used to solve the inverse problem which permits to detect and localize the soft fault. Finally, very acceptable results are obtained and many problems are solved at the same time as: defining the exact position and exact resistance values of the fault, defining the state of electrical network in the real time

Citation

Abderrzak Laib , ,(2017), Soft fault identification in electrical network using time domaine reflectometry and adaptive neuro-fuzzy inference systeme,2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B),Boumerdes, Algeria

he aim the present research is to develop a new approach for thelocalisation of faults in wiring networks based on adaptive neurofuzzy inference system (ANFIS) and time domain reflectometry(TDR). In this approach a forward model has been developed andvalidated with measurements in order to generate a TDR response ofany wiring network then the inverse problem is solved using ANFIS.The developed approach has been tested using a complex configurationthat is YY-shaped network. The results show the efficiency andaccuracy of the proposed approach.

Citation

Abderrzak Laib , , (2017), laib abderrzak [PDF] à partir de wiley.com Full View Localisation of faults in wiring networks using time domain reflectometry and adaptive neuro­fuzzy inference system, Electronics Letters, Vol:53, Issue:9, pages:600–602, The Institution of Engineering and Technology

The modern power electric network is subject to insure more and more complicated functions; the main functions are transfer of energy and information. The faults in wiring cables constitute one of worst problems of power electric network. In practice, the combination of Time Domain Reflectometry (TDR) and wavelet transform is generally used to detect and localize the faults in electric network. Classically, the identification process based on the decomposition of fault signal on details and approximations using Discrete Wavelet Transform (DWT) build some errors both at fault position and in fault nature. For solving this problem a new and improved method which combines the time domain reflectometry, wavelet transform and neural network is proposed in this paper. First, the response of the transmission line is obtained using the Finite Difference Time Domain method (FDTD) applied to transmission line equations, then, the obtained results are analyzed with DWT. Finally, Neural Network method (NN) is applied to solve the inverse problem for reducing the error of fault location affecting the branches of electric network.

Citation

Abderrzak Laib , ,(2017), A new hybrid approach using time-domain reflectometry combined with wavelet and neural network for fault identification in wiring network,2016 8th International Conference on Modelling, Identification and Control (ICMIC)),Algiers, Algeria