Chemseddine Rahmoune

Chemseddine Rahmoune
University M'Hamed Bougara of Boumerdes | UMBB · Département de génie mécanique (DGMEC)

Professor

About

43
Publications
12,606
Reads
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304
Citations
Introduction
Chemseddine Rahmoune currently works at the Département de génie mécanique (DGMEC), University M'Hamed Bougara of Boumerdes. Chemseddine does research in Mechanical Engineering and Electrical Engineering. Their current project is 'Fault feature extraction , selection and classification based on signal processing, optimization algorithmes and machine learning techniques'.
Additional affiliations
December 2018 - present
University M'Hamed Bougara of Boumerdes
Position
  • Maitre de cof classe A
June 2015 - December 2018
University M'Hamed Bougara of Boumerdes
Position
  • MCB
November 2013 - June 2015
University M'Hamed Bougara of Boumerdes
Position
  • MAB

Publications

Publications (43)
Article
Full-text available
Conventional methods (i.e. time and frequency analysis) can routinely be used to reveal gear fault-indicating information in the current signal. In recent years, Wavelet analysis, which can lead to a clear identification of the nature of faults, are widely used to describe rotating machine condition. The Capability of this method in the detection o...
Article
Full-text available
This paper presents a new technique to diagnose differentially two localized gear tooth faults: a pitting and a crack. These faults could have very different prognoses, but existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. In the aim to diagnose differe...
Article
Full-text available
Conventional methods (i.e. time, frequency and cepstrum) can routinely be used to reveal fault-indicating information in the vibration signal. In recent years, wavelet analysis, which can lead to a clear identification of the nature of faults, is widely used to describe rotating machine condition. The capability of this method in the detection of a...
Article
Condition 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...
Article
Full-text available
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-visu...
Article
Misalignment and unbalance are a common fault occurring in the rotor system. A new approach for detecting misalignment and unbalance problems combining the intrinsic time - scale decomposition (ITD), the root mean square (RMS) and perceptron multilayer network (MLP) is proposed in this paper. Vibration signals of normal condition, misalignment hori...
Article
Full-text available
Bearings 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 ope...
Article
Full-text available
Gearboxes 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...
Article
Full-text available
Gear fault diagnosis requires an adaptive decomposition method to extract defect signature. As a self-adaptive approach, local mean decomposition (LMD) decomposes the signal to a set of product functions (PFs). However, LMD suffers from two limits: mode mixing and end effect. To overcome this problem, an optimized technique named “robust LMD (RLMD) u...
Article
Full-text available
Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation condi...
Article
Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of op...
Preprint
Full-text available
Nowadays, multi-fault diagnosis has become the most interesting topic for researchers, since it has lately attracted a substantial attention. The most published works recently have considered defects detection, identification, and classification as the toughest challenge for rotating machinery monitoring. As feature extraction requires robust techn...
Article
Full-text available
Nowadays, multi-fault diagnosis has become the most interesting topic for researchers, since it has lately attracted a substantial attention. The most published works recently have considered defects detection, identification, and classification as the toughest challenge for rotating machinery monitoring. As feature extraction requires robust techn...
Article
Full-text available
Purpose: To demonstrate the correlation between excessive eye rubbing and corneal degeneration for Keratoconus patients. Materials and methods: Keratoconus (KC) patients who regularly rub their eyes had shown a rapid degeneration rate of their affected corneas. This observation is experimentally and numerical discussed and developed based on cli...
Article
Full-text available
In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensit...
Article
Full-text available
Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is...
Conference Paper
Bearings usually operate under harsh conditions which result in a dynamic behavior generating non-stationary vibration signals and overwhelmed by noise. Therefore, bearing fault diagnosis and prognosis become difficult since the purpose is to extract robust features able to detect the appearance of faults, monitoring the degradation of health state...
Article
Full-text available
Condition monitoring of rotating machines has become a more important strategy in structural health monitoring (SHM) research. For fault recognition, the analysis is categorized in two essential main parts: Feature extraction and classification; the first one is used for extracting the information from the signal and the other for decision-making b...
Article
Gear fault diagnosis using vibration signals has become the subject of intensive studies to detect any sudden failure. However, these signals exhibit nonlinear and nonstationary behaviors when the rotating machine operates under multiple working conditions. Furthermore, fault features extraction and classification of multiple gear states are always...
Article
Full-text available
Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal pr...
Conference Paper
Full-text available
The condition monitoring and fault diagnosis of gears is a very important in industrial machinery. In this paper, we propose a new method, by combining the fuzzy entropy of LMD-SVD and Multilayer Perceptron (MLP) neural network to overcome the problem of identification and classification faults in gearbox system. The LMD process allows the vibratio...
Conference Paper
Full-text available
In this paper, we propose a method of automatic detection of bearing defects based on the vibration signal, using singular value decomposition (SVD) and Adaptive Neuro Fuzzy Inference System (ANFIS), to detect the early degradation of bearing defects under different working conditions. Greater accuracy is needed for sensitive areas to avoid all typ...
Conference Paper
Full-text available
Gears are a key element of any mechanical mechanism especially for rotating machines but they are prone to failure. Therefore, identifying the gear damage becomes an indispensable step and gear fault diagnosis takes a huge attention of many researchers. Over the decades, great progress in this field could be noticed and plenty methods have made it...
Article
Full-text available
The condition monitoring and multi-fault diagnosis of rolling bearing is a very important research content in the field of the rotating machinery health management. Most researches widely used empirical mode decomposition in tandem with principal component analysis which is applied for feature extraction. But this method may lead to imprecise class...
Article
Full-text available
Vibration 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 singul...
Conference Paper
Full-text available
In this paper we propose a new detection method using the combination of wavelet packets transform (WPT) and Fast Kurtogram (FK) based on vibration signal in order to detect and monitoring the evolution of spalling defect in gearbox. Wavelet packet was performed for the filtered vibration signal to decompose it into signals with different frequenci...
Conference Paper
Full-text available
Envelope analysis is one of the most used methods in condition monitoring for gear systems to ensure the good performance of the industrial equipment. Combined with empirical mode decomposition (EMD), Hilbert transform and using the kurtosis value, this method is adaptive for non-stationary and non-linear gearbox diagnosis. This approach could succ...
Book
Cet ouvrage met à la disposition des étudiants électromécaniciens un cours sur les notions fondamentales de l'électrotechnique visant à les préparer pour entamer les différentes spécialités de cette vaste discipline. Pour mieux accompagner les étudiants, des exercices corrigés sont présentées à la fin de chaque chapitre. On a commencé par un chapit...
Book
Cet ouvrage met à la disposition des étudiants électromécaniciens un cours sur les notions fondamentales de l'électrotechnique visant à les préparer pour entamer les différentes spécialités de cette vaste discipline. Pour mieux accompagner les étudiants, des exercices sont présentés à la fin de chaque chapitre. Le premier chapitre aborde les circui...
Article
Full-text available
The kurtogram analysis presents some limitations when diagnosing gearbox systems, particularly in time domain. Its envelop signal analysis is not able to detect any defects. This paper presents a new approach to enhance the detection and diagnosis in gearbox systems. This new approach is based on Maximum Correlated Kurtosis Deconvolution combined w...
Article
There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults mak...
Conference Paper
Full-text available
Owing to the relevance and severity of damages caused by rolling bearing faults, the development and application of a robust fault detection methods that offer a high reliable diagnosis in terms of processing and performance are still demanding tasks. In this paper, an application of the empirical wavelet transform (EWT) method is proposed for the...
Conference Paper
Gears are one of the most common mechanisms used for transmitting power and motion in various mechanical applications. Tooth pitting fault is frequently failure modes encountered. An analytical model of one stage spur gearbox is presented where the effects of tooth pitting fault were simulated by magnitude and phase changes in the gearmesh stiffnes...
Article
Full-text available
Hilbert-Huang Transform (HHT) has been renowned for its capacity to reveal fault indicating information issue from vibration signals. It uses Empirical Mode Decomposition (EMD) to decompose a signal accordingly to its contained information into a set of Intrinsic Mode Functions (IMFs). Then, the instantaneous frequencies are performed of each IMF u...

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