Browsing by Author "Merainani, Boualem"

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Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions
(JVE International, 2019) Gougam, Fawzi; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem
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 based on these features. A higher accuracy is needed for sensitive places to avoid all kinds of damages that can lead to economic losses and it may affect the human safety as well. In this paper, we propose a new hybrid and automatic approach for bearing faults diagnosis. This method uses a combination between Empirical wavelet Transform (EWT) and Fuzzy logic System (FLS), in order to detect and localize the early degradation of bearing state under different working conditions. EWT build a wavelet filter bank to extract amplitude modulated-frequency modulated component of signal. Modes presenting a high impulsiveness is then selected using the kurtosis indicator. Thereafter, time domain features (TDFs) are applied for the reconstructed signal to extract the fault features which are finally used as an inputs of FLS in order to identify and classify the bearing states. The experimental results shows that the proposed method can accurately extract and classify the bearing fault under variable conditions. Moreover, performance of EWT and empirical mode decomposition (EMD) are studied and shows the superiority of the proposed method
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.
Contribution to the monitoring of rotating machines using signal approach
(2017) Merainani, Boualem
Now more than ever, the global competition have changed the way manufacturing companies operate. These changes have affected maintenance and made its role even more crucial for business success. To remain competitive, the manufacturing companies are forced to continuously increase the efficiency and the profitability of their facilities that are growing in complexity. Rotating machines cover a wide range of these facilities. However, no matter how well they are designed and manufactured, as the service time goes by, various problems will appear, blocking the running of the machine and causing serious accidents inevitably. Vibration-based condition monitoring aim is to detect the initiation of faults and symptoms related to their different degradation conditions. In this thesis, a novel methodology for rotating machines diagnosis has been developed which combines a self adaptive time-frequency analysis method, Hilbert empirical wavelet transform, and the singular value decomposition. The fault feature extraction and classification method has been verified using results from both dynamic modeling and simulation of an electromechanical system and test rigs. The lectromechanical system comprising a three phase induction motor coupled with a single stage spur gearbox with effects of faults due to 45 shaft slant crack, tooth cracking, and tooth surface pitting. Experiments have been conducted on the data sets extracted from two tests campaigns and collected at different speed and load conditions. The first one consists of a gearbox with five pinion fault types while the second one is an induction motor driven mechanical system with three fault statuses in the bearing
Detection of bearing fault using Empirical Wavelet Transform and S Transform methods
(IEEE, 2020) Merainani, Boualem; Bouzid, Abir Amar; Ratni, Azeddine; Benazzouz, Djamel
Rolling-element bearing is one of the crucial mechanical components in induction motors. Since, its fault may produce huge damage; the way to efficiently diagnose the bearing faults is a high issue in signal processing, and its fault detection draw an important significance. In this paper, a hybrid method based on Empirical Wavelet Transform and S Transform has been proposed in order to detect the outer race bearing fault in an induction motor using vibration signals. As the collected signals are disturbed by noise, EWT is used to filter the raw signals in conjunction with isolating the region containing fault characteristic frequencies. Then ST is used to represent an Amplitude-Frequency and a Time-Frequency contour of the filtered signals, which allow to detect the bearing fault. Finally, experimental vibration data have been investigated to assess the reliability of the developed method. The results obtained show a good performance
Detection of shaft crack fault in gearbox using hilbert transforms
(IEEE, 2017) Ratni, Azeddine; Rahmoune, Chemseddine; Benazzouz, Djamel; Ould bouamama, Belkacem; Merainani, Boualem
Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform
(Sage, 2017) Merainani, Boualem; Benazzouz, Djamel; Rahmoune, Chemseddine
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
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, Semchedine
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 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 extraction
New gear fault diagnosis method based on MODWPT and neural network for feature extraction and classification
(ASTM International, 2019) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem; Fedala, Semchedine
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 unsatisfactory and considered as a huge task. This is the main reason that motivates us to develop a new intelligent gear fault diagnosis method in order to automatically identify and classify several kinds of gear defects under different work conditions. So in this article, we propose a combination between the maximal overlap discrete wavelet packet transform (MODWPT), entropy indicator, and a multilayer perceptron (MLP) neural network as a new automatic fault diagnosis approach. MODWPT decomposes the data signal into several components using a uniform frequency bandwidth. Each decomposed component is selected to extract feature vector using entropy indicator. Finally, MLP provides a powerful automatic tool for identifying and classifying the aforementioned extracted features. Experimental vibration signals of healthy gear; gear with general surface wear; gear with chipped tooth in length; gear with chipped tooth in width; gear with missing tooth; and gear with tooth root crack are recorded under fifteen different work conditions to test the effectiveness of the suggested technique. Experimental results affirm that our proposed approach can successfully detect, identify, and classify the gear fault pattern in all cases
Rolling bearing fault diagnosis based empirical wavelet transform using vibration signal
(IEEE, 2017) Merainani, Boualem; Rahmoune, Chemseddine; Benazzouz, Djamel; Ould-Bouamama, Belkacem