M. LAIB Abderrzak

MCA

Directory of teachers

Department

BASE COMMON ST Departement ST

Research Interests

Compatibilité Électromagnétique Dans Les Systèmes Électriques

Contact Info

University of M'Sila, Algeria

Email : abderrzak.laib@univ-msila.dz

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Recent Publications

2025-01-07

Topology reconstruction of wiring networks using an iterative process based on Time-Domain Reflectometry and Forensic-Based Investigation algorithm

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

M. LAIB Abderrzak, (2025-01-07), "Topology reconstruction of wiring networks using an iterative process based on Time-Domain Reflectometry and Forensic-Based Investigation algorithm", [national] Nondestructive Testing and Evaluation , taylor and francis

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2024-07-06

Real-time detection of bearing faults through a hybrid WTMP analysis of frequency-related states

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

M. LAIB Abderrzak, (2024-07-06), "Real-time detection of bearing faults through a hybrid WTMP analysis of frequency-related states", [national] International Journal of Dynamics and Control , Springer International Publishing

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2024-03-07

Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

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

M. LAIB Abderrzak, (2024-03-07), "Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine", [national] IET Science, Measurement & Technolog , iet

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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

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

M. LAIB Abderrzak, (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", [national] Electrical Engineering , Springer Berlin Heidelberg

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2023-11-07

Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids

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

M. LAIB Abderrzak, (2023-11-07), "Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids", [national] Engineering applications of artificial intelligence , Sciencedirect

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2022

Performances Evaluation of Automatic Authorship Attribution on Ancient Arabic Documents

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

M. LAIB Abderrzak, Halim Sayoud, , (2022), "Performances Evaluation of Automatic Authorship Attribution on Ancient Arabic Documents", [international] International Conference of advanced Technology in Electronic and Electrical Engineering , University of Msila

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2021

A Frequency Independent Technique to Estimate Harmonics and Interharmonics in Shipboard Microgrids

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

M. LAIB Abderrzak, (2021), "A Frequency Independent Technique to Estimate Harmonics and Interharmonics in Shipboard Microgrids", [national] IEEE Transactions on Smart Grid , IEEE

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2020

A resolution-enhanced sliding matrix pencil method for evaluation of harmonics distortion in shipboard microgrids

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

M. LAIB Abderrzak, (2020), "A resolution-enhanced sliding matrix pencil method for evaluation of harmonics distortion in shipboard microgrids", [national] IEEE Transactions on Transportation Electrification , IEEE

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2019

Soft fault identification in electrical network using time domain reflectometry and neural network

Time Domain Reflectometry (TDR) is commonly used to detect and localize hard faults in electric network. Unfortunately, in the case of soft fault especially in the case of complex network (network with several branches) it remains very difficult to detect the affected branch. In order to resolve this problem, we propose a new approach based on the Time Domain Reflectometry combined with Neural Network method (NN); the response of the electric network is obtained by applying the Finite Difference Time Domain method
(FDTD) on the transmission line equations and the inverse problem is solved using Neural Network, very acceptable results are obtained basing on our new strategy which is capable to: define the fault by given the correct value of both of resistance and position, define the state of electrical network online, detect and localize more than one soft fault.
Citation

M. LAIB Abderrzak, (2019), "Soft fault identification in electrical network using time domain reflectometry and neural network", [national] Advanced Control Engineering Methods in Electrical Engineering Systems , Springer International Publishing

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Effective and low‐cost passive compensator system to improve the power quality of two electric generators

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

M. LAIB Abderrzak, (2019), "Effective and low‐cost passive compensator system to improve the power quality of two electric generators", [national] IET Power Electronics , The Institution of Engineering and Technology

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2017

A new hybrid approach using time-domain reflectometry combined with wavelet and neural network for fault identification in wiring network

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

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

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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

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

M. LAIB Abderrzak, (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", [national] Electronics Letters , The Institution of Engineering and Technology

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Soft fault identification in electrical network using time domaine reflectometry and adaptive neuro-fuzzy inference systeme

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

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

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