Browsing by Author "Bettahar, Toufik"

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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, Djamel
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 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 efficiency
Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
(SAGE, 2021) Mahami, Amine; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
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-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.
Keratoconus prognosis study for patients with corneal external mechanical stress mode
(Springer Nature, 2020) Bettahar, Toufik; Rahmoune, Chemseddine; Benazzouz, Djamel
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 clinical data of 8 of KC Patients with a mean age of 26.5 ± 9.4 years old, and four healthy individuals with a mean age of 24.33 ± 5 years old at the baseline. Corneal topography was used to measure both central corneal thickness (CCT) and its total refractive power. The registered data had been exploited to assess the progression of the disease, and the final results were embedded in a finite element model of human corneas to simulate their response to eye rubbing at different stages of the pathology. Corneal lifetime prognosis using multi-layer perceptron was then established to estimate the number of eye rubbing cycles for each stage of KC. Results The survey of KC patients who declared stopping eye rubbing had shown a decrease in CCT loss rate, followed by a durable stability. Mechanical stresses numerical simulations had shown different corneal behaviours in term of shape deformity, apical raise and corneal applanation between healthy and KC stages models. Apical rise ranged from 0.122 to 0.389 mm for an applied intraocular pressure that equals to 15 mmHg. A normal stress of 5 kPa provoked a corneal applanation that ranged from 0.27 mm in healthy cases to 1.173 mm in severe stages of the disease. The application of 2.5 kPa biaxial stress had resulted normal and tangential applanations that successively ranged from 0.152 and 0.173 mm in healthy corneas to 0.446 mm and 0.458 mm in severe KC stages. An adopted prognosis algorithm was able to predict the current stage of the disease and to estimate the remaining number of eye rubbing cycles before failure. Conclusion Eye rubbing was proven to be a considerable contributing factor in KC patient’s corneal degeneration. The progression of this pathology could be decreased or halted by stopping eye rubbing at early stages.
Keratoconus prognosis study for patients with corneal external mechanical stress mode
(Université M'Hamed Bougara : Faculté de Technologie, 2021) Bettahar, Toufik; Benazzouz, Djamel(Directeur de thèse)
A corneal numerical model was developed based on experimental previous studies, and gathered data from EKBASSIRA eye clinic. The cornea is considered a two layers 3D viscoelastic solid. Uniaxial and multiaxial cyclic loads has been applied on the Keratoconus and healthy corneas in order the emphasize the influence of eye rubbing on corneal applanation, loss of shape and biomechanical properties .A Finite element analysis simulation results have shown a significant difference between healthy and KC stages responses to those external loads, for a fixed intraocular pressure .Life time prognosis and classification algorithms using analytical life time computation and Artificial Neural Network approaches was then established to shed light on the severity of eye rubbing on the evolution and the progression rate of Keratoconus and its offset, for patients with subclinical, mild and advanced forms of the disease
New criteria for wrapper feature selection to enhance bearing fault classification
(SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Meddour, Ikhlas; Bettahar, Toufik; Zair, Mohamed
Classification is a critical task in many fields, including signal processing and data analysis. The accuracy and stability of classification results can be improved by selecting the most relevant features from the data. In this paper, a new criterion for feature selection using wrapper method is proposed, which is based on the evaluation of the classification results according to the accuracy and stability (standard deviation) of each class and the number of selected features. The pro- posed method is evaluated using Random Forest (RF) and Ant Colony Optimization (ACO) algorithms on a benchmark dataset. Results show that the proposed method outperforms classical feature selection methods in terms of accuracy and stability of classification results, especially for the difficult-to-classify combined damage class. This study demon- strates the effectiveness of the proposed new wrapper feature selection criterion to improve the performance of classifi- cation algorithms with higher stability (STD: C1 = 0.5, C2 = 0.8, C3 = 0.6, C4 = 1.8) and better accuracy (average C1 = 98.5%, C2 = 96.6%, C3 = 9.5%, C4 = 93) for the both; the statoric current and the vibration signal compared to other techniques. Machine learning methods had proven their efficiency in time-varying machines fault diagnosis when taking vibration signals and statoric currents extracted features as inputs. However, the use of the both demonstrated a higher robustness and a remarkable superiority.
New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric
(SAGE Publications, 2020) Bettahar, Toufik; Rahmoune, C.; Benazzouz, D.; Merainani, B.
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 sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram
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, Djamel
Electrical 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 problematics